Publications
- Highlighted papers for our work on machine learning for medicine
- Highlighted papers for our work on machine learning for education
- Journals [Switch to topic order]
- Selected Conference Papers
- Books
- Chapters
- Technical Reports

 Books

2. M. van der Schaar and P. Chou, editors, "Multimedia over IP and Wireless Networks: Compression, Networking, and Systems," Academic Press, 2007.
1. M. van der Schaar, D. S. Turaga and T. Stockhammer, "MPEG-4 beyond video compression: Object Coding, Scalability and Error Resilience," Digital Library of Computer Science and Engineering, Morgan Claypool, 2005.

    Journals [Switch to topic order]

  1. A. M. Alaa, M. van der Schaar, "Prognostication and Risk Factors for Cystic Fibrosis via Automated Machine Learning," Scientific Reports, 2018. [Link]

  2. N. Akbarzadeh, C. Tekin, M. van der Schaar "Online Learning in Limit Order Book Trade Execution," accepted and to appear in IEEE Transactions on Signal Processing (TSP), 2018.

  3. H. S. Lee, C. Tekin, M. van der Schaar, J. W. Lee "Adaptive Contextual Learning for Unit Commitment in Microgrids with Renewable Energy Sources," accepted and to appear in IEEE Journal of Selected Topics in Signal Processing (JSTSP), 2018.

  4. A. M. Alaa, M. van der Schaar, "Bayesian Nonparametric Causal Inference: Information Rates and Learning Algorithms," accepted and to appear in IEEE Journal of Selected Topics in Signal Processing (JSTSP), 2018. [Link]

  5. A. Bellot, M. van der Schaar, "A Hierarchical Bayesian Model for Personalized Survival Predictions," accepted and to appear in IEEE J. Biomedical and Health Informatics, 2018. [Link] [Supplementary Materials]

  6. E. Cenko, J. Yoon, S. Kedev, G. Stankovic, Z. Vasiljevic, G. Krljanac, O. Kalpak, B. Ricci, D. Milicic, O. Manfrini, M. van der Schaar, L. Badimon, R. Bugiardini, "Sex Differences in Outcomes After STEMI: Effect Modification by Treatment Strategy and Age," accepted and to appear in JAMA Internal Medicine, 2018. [Link]

  7. J. Yoon, W. R. Zame, A. Banerjee, M. Cadeiras, A. Alaa, M. van der Schaar, "Personalized survival predictions via Trees of Predictors: An application to cardiac transplantation," accepted and to appear in PloS One, 2018. [Link] [Calculator Link]

  8. S. Muller, C. Tekin, M. van der Schaar, A. Klein, "Context-Aware Hierarchical Online Learning for Performance Maximization in Mobile Crowdsourcing,; accepted and to appear in IEEE/ACM Transactions on Networking, 2018.

  9. O. Atan, C. Tekin, M. van der Schaar, "Global Bandits; accepted and to appear in IEEE Transactions on Neural Networks and Learning Systems (TNNLS), 2018.

  10. J. Yoon, W. R. Zame, M. van der Schaar, "ToPs: Ensemble Learning with Trees of Predictors," accepted and to appear in IEEE Transactions on Signal Processing (TSP), 2018. [Link]

  11. C. Shen, C. Tekin, M. van der Schaar, "Generalized Global Bandit and Its Application in Cellular Coverage Optimization," accepted and to appear in IEEE Journal of Selected Topics in Signal Processing, 2018.

  12. A. M. Alaa, M. van der Schaar, "A Hidden Absorbing Semi-Markov Model for Informatively Censored Temporal Data: Learning and Inference," accepted and to appear in Journal of Machine Learning Research (JMLR), 2017. [Link]

  13. M. K. Ross, J. Yoon, M. van der Schaar, "Discovering Pediatric Asthma Phenotypes Based on Response to Controller Medication Using Machine Learning," accepted and to appear in Annals of the American Thoracic Society, 2017. [Link]

  14. R. Hellman, C. Tekin, M. van der Schaar, V. Santos, "Functional Contour-following via Haptic Perception and Reinforcement Learning," accepted and to appear in IEEE Transactions on Haptics, 2017.

  15. A. Alaa, K. Ahuja, and M. van der Schaar, "A Micro-foundation of Social Capital in Evolving Social Networks," accepted and to appear in IEEE Transactions on Network Science and Engineering, 2017. [Link]

  16. A. M. Alaa, J. Yoon, S. Hu, and M. van der Schaar, "Personalized Risk Scoring for Critical Care Prognosis using Mixtures of Gaussian Processes," accepted and to appear in IEEE Transactions on Biomedical Engineering., 2017. [Link]

  17. C.-K. Yu, M. van der Schaar, and A. Sayed, "Distributed Learning for Stochastic Generalized Nash Equilibrium Problems," accepted and to appear in IEEE Transactions on Signal Processing., 2017. [Link]

  18. J. Xu, K. H. Moon, and M. van der Schaar, "A Machine Learning Approach for Tracking and Predicting Student Performance in Degree Programs," accepted and to appear in IEEE Journal of Selected Topics in Signal Processing., 2017. [Link]

  19. S. Zhang and M. van der Schaar, "From Acquaintances to Friends: Homophily and Learning in Networks," accepted and to appear in the 2017 JSAC Game Theory for Networks special issue., 2017. [Link]

  20. C. Wu, M. Gerla, and M. van der Schaar, "Social Norm Incentives for Network Coding in MANETs," accepted and to appear in IEEE/ACM Trans. Networking., 2016.

  21. S. Muller, O. Atan, M. van der Schaar and A. Klein, "Context-Aware Proactive Content Caching With Service Differentiation in Wireless Networks," in IEEE Transactions on Wireless Communications, vol. 16, no. 2, pp. 1024-1036, Feb. 2017. [Link]

  22. C. Tekin, J. Yoon, and M. van der Schaar, "Adaptive Ensemble Learning with Confidence Bounds," accepted and to appear in IEEE Trans. Signal Process., 2016. [Link]

  23. A. Alaa, K. H. Moon, W. Hsu and M. van der Schaar, "ConfidentCare: A Clinical Decision Support System for Personalized Breast Cancer Screening," accepted and to appear in IEEE Transactions on Multimedia - Special Issue on Multimedia-based Healthcare, 2016. [Link]

  24. C. Shen, C. Tekin, and M. van der Schaar, "A Non-stochastic Learning Approach to Energy Efficient Mobility Management," accepted and to appear in IEEE J. Sel. Areas Commun., Series on Green Communications and Networking, 2016. [Link]

  25. S. Li, J. Xu, M. van der Schaar, and W. Li, "Trend-Aware Video Caching through Online Learning, " accepted and to appear in IEEE Transactions on Multimedia, 2016. [Link]

  26. Y. Xiao and M. van der Schaar, "Foresighted Demand Side Management, " accepted and to appear in IEEE Transactions on Smart Grids, 2016. [Link]

  27. H. Li, K. Sudusinghe, Y. Liu, J. Yoon, M. van der Schaar, E. Blasch, and S. S. Bhattacharyya, "Dynamic, Data-Driven Processing of Multispectral Video Streams, " to appear in IEEE Aerospace and Electronic Systems Magazine (Feature Article), vol. 32, no. 7, pp. 50-57, Sep. 2017. [Link]

  28. J. Xu, T. Xiang and M. van der Schaar, "Personalized Course Sequence Recommendations, " IEEE Transactions on Signal Processing, vol. 64, no. 20, pp. 5340-5352, Oct. 2016. [Link]

  29. E. Soltanmohammadi, M. Naraghi-Pour, and M. van der Schaar, " Context-based Unsupervised Ensemble Learning and Feature Ranking," accepted and to appear in Machine Learning, pp. 1-27, June 2016. [Link]

  30. J. Yoon, C. Davtyan, and M. van der Schaar, "Discovery and Clinical Decision Support for Personalized Healthcare," accepted and to appear in IEEE J. Biomedical and Health Informatics, 2016. [Link]

  31. Y. Xiao, F Dorfler, and M. van der Schaar, "Incentive Design in Peer Review: Rating and Repeated Endogenous Matching," accepted and to appear in IEEE Transactions on Network Science and Engineering, 2016. [Link]

  32. J. Xu, J. Y. Xu, L. Song, G. Pottie, and M. van der Schaar, "Personalized Active Learning for Activity Classification using Wireless Wearable Sensors," IEEE Journal on Selected Topics in Signal Processing, vol. 10, no. 5, pp. 865-876, Apr. 2016. [Link]

  33. K. Kanoun, C. Tekin, D. Atienza, and M. van der Schaar, "Big-Data Streaming Applications Scheduling Based on Staged Multi-armed Bandits," accepted and to appear in IEEE Transactions on Computers, 2016. [Link] [Supplementary material]

  34. S. Amuru, R. M. Buehrer, and M. van der Schaar, "Blind Network Interdiction Strategies- A Learning Approach," IEEE Transactions on Cognitive Communications and Networking, vo. 1, no. 4, pp. 435-449, Mar. 2016.[Link]

  35. S. Amuru, C. Tekin, M. van der Schaar and M. Buehrer, "Jamming Bandits - A Novel Learning Method for Optimal Jamming," IEEE Transactions on Wireless Communications, vol. 15, no. 4, pp. 2792-2808, Apr. 2016. [Link]

  36. L. Canzian, U. Demiryurek, and M. van der Schaar, "Collision Detection by Networked Sensors," IEEE Transactions on Signal and Information Processing over Networks, vol. 2, no. 1, pp. 1-15, Mar. 2016. [Link]

  37. A. Alaa, K. Ahuja, M. van der Schaar, " Self-organizing Networks of Information Gathering Cognitive Agents," IEEE Transactions on Cognitive Communications and Networking - Inaugural issue (invited paper), vol. 1, no. 1, pp. 100-112, Nov. 2015. [Link]

  38. Y. Meier, J. Xu, O. Atan, and M. van der Schaar, "Predicting Grades," IEEE Transactions on Signal Processing, vol. 64, no. 4, pp. 959-972, Feb. 2016. [Link]

  39. B.-G. Kim, Y. Zhang, M. van der Schaar, and J.-W. Lee, "Dynamic Pricing and Energy Consumption Scheduling with Reinforcement Learning," accepted and to appear in IEEE Transactions on Smart Grid, 2015. [Link]

  40. L. Canzian, M. Zorzi, M. van der Schaar, "Game Theoretic Design of MAC Protocols: Pricing versus Intervention," IEEE Trans. on Communications, vol. 63, no. 11, pp. 4287-4303, 2015.[Link]

  41. L. Canzian, Y. Zhang, M. van der Schaar, "Ensemble of Distributed Learners for Online Classification of Dynamic Data Streams," IEEE Trans. on Signal and Information Processing over Networks, vol. 1, no. 3, 2015. [Link]

  42. C.-K. Yu, M. van der Schaar, and A. H. Sayed, "Information-Sharing over Adaptive Networks with Self-interested Agents," IEEE Trans. on Signal and Information Processing over Networks, vol. 1, no. 1, pp. 2-19, Jun. 2015. [Link]

  43. Z. Yuan, J. Xu, Y. Xue, and M. van der Schaar, "Bits Learning: User-adjustable Privacy versus Accuracy in Internet Traffic Classification," IEEE Communications Letters, vol. 20, no. 4, Apr. 2016. [Link]

  44. C. Tekin and M. van der Schaar, "Active Learning in Context-Driven Stream Mining with an Application to Image Mining," IEEE Trans. Image Process., vol. 24, no. 11, 2015. [Link]

  45. L. Canzian, K. Zhao, G. C. Wong, M. van der Schaar, "A Dynamic Network Formation Model for Understanding Bacterial Self-Organization into Micro-Colonies," IEEE Transactions on Molecular, Biological, and Multi-Scale Communications, vol. 1, no. 1, pp. 76 - 89, 2015. [Link]

  46. J. Xu and M. van der Schaar, "Incentive-Compatible Demand-Side Management for Smart Grids based on Review Strategies," EURASIP Journal on Advances in Signal Processing - Topical collection: Advanced signal processing techniques and telecommunications network infrastructures for Smart Grid analysis, monitoring and management, 2015. [Link]

  47. M. van der Schaar, Y. Xiao, W. Zame, "Efficient Outcomes in Repeated Games with Limited Monitoring," Economic Theory, vol. 60, no. 1, pp. 1-34, 2015 - Lead article. [Link]

  48. M. Wolf, M. van der Schaar, H. Kim and J. Xu, "Analysis and Decision-Making in Caring Environments for Adults with Special Needs Adults," IEEE Design & Test, Special Issue on Cyber-Physical systems for Medical Applications, vol. 32, no. 5, pp. 35-44, 2015. [Link]

  49. J. Xu, C. Tekin, S. Zhang and M. van der Schaar, "Distributed Multi-Agent Online Learning Based on Global Feedback," IEEE Trans. Signal Process. vol. 63, no. 9, Feb 2015. [Link]

  50. K. Ahuja, Y. Xiao and M. van der Schaar, "Efficient Interference Management Policies for Femtocell Networks," IEEE Transactions on Wireless Communications, vol. 14, no. 9, pp. 4879-4893, Sept. 2015. [Link]

  51. K. Ahuja, Y. Xiao and M. van der Schaar "Distributed Interference Management Policies for Heterogeneous Small Cell Networks," IEEE J. Sel. Areas Commun., vol. 33, no. 6, pp. 1112-1126, 2015. [Link]

  52. C. Tekin and M. van der Schaar, "Distributed Online Learning via Cooperative Contextual Bandits," IEEE Trans. Signal Process., vol. 63, no. 14, pp. 3700-3714, 2015. [Link]

  53. N. Mastronarde, V. Patel, J. Xu, L. Liu, and M. van der Schaar , "To Relay or Not to Relay: Learning Device-to-Device Relaying Strategies in Cellular Networks,"IEEE Transactions on Mobile Computing, vol. 15, no. 6, pp. 1569-1585, June 2016. [Link]

  54. L. Song, W. Hsu, J. Xu and M. van der Schaar, "Using contextual learning to improve diagnostic accuracy: application in breast cancer screening," accepted and to appear in IEEE J. Biomedical and Health Informatics, 2015. [Link]

  55. C. Shen, J. Xu and M. van der Schaar, "Silence is Gold: Strategic Interference Mitigation Using Tokens in Heterogeneous Small Cell Networks," IEEE J. Sel. Areas Commun., vol.33, no.6, pp1097-1111, June 2015. [Link]

  56. C. Tekin and M. van der Schaar, "Contextual Online Learning for Multimedia Content Aggregation," IEEE Trans. Multimedia,vol. 17, no. 4, pp. 549-561, Feb. 2015. [Link]

  57. C. Tekin and M. van der Schaar, "RELEAF: An Algorithm for Learning and Exploiting Relevance," IEEE Journal of Selected Topics in Signal Processing, Special Issue on Signal Processing for Big Data, vol. 9, no. 4, pp. 716-727, June 2015. [Link]

  58. J. Xu, D. Deng, U. Demiryurek, C. Shahabi and M. van der Schaar, "Mining the Situation: Spatiotemporal Traffic Prediction with Big Data," IEEE Journal of Selected Topics in Signal Processing, Special Issue on Signal Processing for Big Data, vol. 9, no.4, pp. 702-715, June 2015. [Link]

  59. Y. Song and M. van der Schaar, "Dynamic Network Formation with Incomplete Information," Economic Theory, vol. 59, no. 2, pp. 301-331, 2015. [Link]

  60. J. Xu and M. van der Schaar, "Efficient Working and Shirking in Networks," IEEE JSAC Bonus Issue for Emerging Technologies, vol. 33, no. 4, pp. 651-662, April 2015. [Link]

  61. N. Thomos, E. Kurdoglu, P. Frossard, and M. van der Schaar, "Adaptive Prioritized Random Linear Coding and Scheduling for Layered Data Delivery from Multiple Servers," IEEE Trans. Multimedia, vol. 17, no. 6, pp. 893-906, June 2015. [Link]

  62. Y. Xiao and M. van der Schaar, "Socially-Optimal Design of Service Exchange Platforms with Imperfect Monitoring," ACM Transactions on Economics and Computation, vol. 3, no. 4, Jul. 2015. [Link]

  63. L. Canzian and M. van der Schaar, "Timely Event Detection by Networked Learners," IEEE Transactions on Signal Processing, vol. 63, no. 5, pp. 1282-1296, Jan. 2015. [Link]

  64. C. Tekin, O. Atan and M. van der Schaar, "Discover the Expert: Context-Adaptive Expert Selection for Medical Diagnosis," IEEE Transactions on Emerging Topics in Computing, vol. 3, no. 2, pp. 220-234, June 2015. [Link]

  65. M. van der Schaar and S. Zhang, "A Dynamic Model of Certification and Reputation," Economic Theory, vol. 58, no. 3, pp. 509-541, Oct. 2014. [Link]

  66. L. Song, C. Tekin, and M. van der Schaar, "Online Learning in Large-scale Contextual Recommender Systems," accepted and to appear in IEEE Transactions on Services Computing. [Link]

  67. J. Xu, M. van der Schaar, J. Liu and H. Li, "Forecasting Popularity of Videos using Social Media," IEEE Journal of Selected Topics in Signal Processing (JSTSP), vol. 9, no. 2, pp. 330-343, Nov. 2014.. [Link]

  68. S. Ren and M. van der Schaar, "Dynamic Scheduling for Energy Minimization in Delay-Sensitive Stream Mining," IEEE Transactions on Signal Processing, vol. 62, no. 20, 2014. [Link]

  69. Y. Zhang and M. van der Schaar, "Structure-Aware Stochastic Storage Management In Smart Grids," IEEE Journal of Selected Topics in Signal Processing, Special Issue on Signal Processing in Smart Electric Power Grid, vol. 8, no. 6, pp. 1098-1110, 2014. [Link]

  70. M. Alizadeh, Y. Xiao, A. Scaglione, and M. van der Schaar, "Dynamic Incentive Design for Participation in Direct Load Scheduling Programs," IEEE Journal of Selected Topics in Signal Processing, Special Issue on Signal Processing in Smart Electric Power Grid, vol. 8, no. 6, pp. 1111-1126, 2014. [Link]

  71. J. Alcaraz and M. van der Schaar, "Coalitional Games with Intervention: Application to Spectrum Leasing in Cognitive Radio," IEEE Transactions on Wireless Communications, vol. 13, no. 11, pp. 6166-6179, 2014. [Link]

  72. Y. Xiao and M. van der Schaar, "Optimal Foresighted Multi-User Wireless Video," IEEE J. Sel. Topics Signal Process., Special Issue on Visual Signal Processing for Wireless Networks., vol. 9, no. 1, pp. 89-101, Feb. 2015.

  73. Y. Xiao, W. R. Zame and M. van der Schaar, "Technology Choices and Pricing Policies in Public and Private Wireless Networks," IEEE Transactions on Wireless Communications,vol. 13, no. 12, Oct. 2014. [Link]

  74. L. Canzian and M. van der Schaar, "Real-time stream mining: online knowledge extraction using classifier networks," IEEE Network Magazine, Special Issue on Networking for Big Data, vol. 29, no. 5, pp. 10-16 Oct. 2015. [Link]

  75. O. Atan, A. Yiannis, C. Tekin, and M. van der Schaar, "Bandit Framework For Systematic Learning In Wireless Video-Based Face Recognition," IEEE J. Sel. Topics Signal Process., vol. 9, no. 1, June. 2014. [Link]

  76. C. Tekin, S. Zhang, and M. van der Schaar, "Distributed Online Learning in Social Recommender Systems," IEEE Journal of Selected Topics in Signal Processing (JSTSP), vol. 8, no. 4, pp. 638-652, Aug. 2014. [Link]

  77. J. Xu, Y. Song, and M. van der Schaar, "Sharing in Networks of Strategic Agents," IEEE J. Sel. Topics Signal Process. - Special issue on "Signal Processing for Social Networks", vol. 8, no. 4, pp. 717-731, Aug. 2014. [Link]

  78. K. Kanoun, N. Mastronarde, D. Atienza, and M. van der Schaar, "Online Energy-Efficient Task-Graph Scheduling for Multicore Platforms," IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, vol. 33, no. 8, pp. 1194-1207, Aug. 2014. [Link]

  79. L. Song, Y. Xiao, and M. van der Schaar, "Demand Side Management in Smart Grids using a Repeated Game Framework," IEEE J. Sel. Areas Commun., Special issue on Smart Grid Communications Series,vol. 32, no. 7, pp. 1412-1424, July 2014. [Link]

  80. Y. Zhang and M. van der Schaar, "Collective Ratings for Online Communities with Strategic Users," IEEE Transactions on Signal Processing, vol. 62, no. 12, pp. 3069-3083, June 2014. [Link]

  81. S. Parsaeefard, A. R. Sharafat and M. van der Schaar, "Robust Power Control for Heterogeneous Users in Shared Unlicensed Bands," IEEE Transactions on Wireless Communications, vol. 13, no. 6, pp. 3167-3182, June 2014. [Link]

  82. J. Xu, Y. Andreopoulos, Y. Xiao and M. van der Schaar, "Non-stationary Resource Allocation Policies for Delay-constrained Video Streaming: Application to Video over Internet-of-Things-enabled Networks," J. Sel. Areas in Commun., Special Issue on Adaptive Media Streaming, vol. 32, no. 4, pp. 782-794, Apr. 2014. [Link]

  83. Y. Xiao and M. van der Schaar, "Energy-efficient Nonstationary Spectrum Sharing," IEEE Trans. on Communications, vol. 62, no. 3, pp. 810-821, March 2014. [Link]

  84. S. Parsaeefard, A. R. Sharafat and M. van der Schaar, "Robust Additively Coupled Games in the Presence of Bounded Uncertainty in Communication Networks," IEEE Trans. on Vehicular Technology, vol. 63, no. 3, pp. 1436-1452, March 2014. [Link]

  85. Y. Zhang, J. Park, M. van der Schaar, “Rating Protocols for Online Communities", ACM Transactions on Economics and Computation, vol. 2, no. 1, March 2014. [Link]
  86. W. Zame, J. Xu and M. van der Schaar, "Cooperative Multi-Agent Learning and Coordination for Cognitive Radio Networks," IEEE J. Sel. Areas Commun. - Special issue on Cognitive Radio Series, vol. 32, no. 3, pp. 464-477, Mar. 2014. [Link]
  87. M. van der Schaar, J. Xu and W. Zame, "Efficient Online Exchange via Fiat Money," in Economic Theory, vol. 54, no. 2, pp. 211-248, Oct. 2013. [Link]

  88. W. Zame, J. Xu and M. van der Schaar, "Winning the Lottery: Learning Perfect Coordination with Minimal Feedback," in IEEE J. Sel. Topics in Signal Process., vol. 7, no. 5, pp. 846-857, Oct. 2013. [Link]

  89. Y. Zhang and M. van der Schaar, "Robust Reputation Protocol Design for Online Communities: A Stochastic Stability Analysis," in IEEE J. of Sel. Topics in Signal Process., vol. 7, no. 5, pp. 907-920, Oct. 2013. [Link]

  90. H.P. Shiang and M. van der Schaar, "Conjecture-Based Load Balancing for Delay-Sensitive Users Without Message Exchanges," in IEEE Trans. on Vehicular Technology, vol. 62, no. 8, pp. 3983-3995, Oct. 2013. [Link]

  91. Y. Zhang, M. van der Schaar, "Incentive Provision and Job Allocation in Social Cloud Systems", in IEEE J. Sel. Areas in Commun vol. 31, no. 9, pp. 607-617, Sep. 2013. [Link]
  92. K.T. Phan, T. Le-Ngoc, M. van der Schaar, and F. Fu, "Optimal Scheduling over Time-Varying Channels with Traffic Admission Control: Structural Results and Online Learning Algorithms," in IEEE Trans. on Wireless Communication., vol. 12, no. 9, pp. 4434-4444, Sep. 2013. [Link]

  93. L. Canzian, Y. Xiao, W. Zame, M. Zorzi, and M. van der Schaar, "Intervention with Complete and Incomplete Information: Application to Flow Control," in IEEE Trans. Commun., vol. 61, no. 8, pp. 3206-3218, Aug. 2013. [Link]

  94. L. Canzian, Y. Xiao, W. Zame, M. Zorzi, and M. van der Schaar, "Intervention with Private Information, Imperfect Monitoring and Costly Communication," in IEEE Trans. Commun., vol. 61, no. 8, pp. 3192-3205, Aug. 2013. [Link]

  95. J. Xu and M. van der Schaar, "Token System Design for Autonomic Wireless Relay Networks," in IEEE Trans. on Commun., vol. 61, no. 7, pp. 2924-2935, July 2013. [Link]

  96. B.-G. Kim, S. Ren, M. van der Schaar, and J.-W. Lee, "Bidirectional Energy Trading and Residential Load Scheduling with Electric Vehicles in the Smart Grid," in IEEE J. Sel. Areas Commun. - Special issue on Smart Grid Communications Series, vol. 31, no. 7, pp. 1219-1234, July 2013. [Link]

  97. Y. Zhang and M. van der Schaar, "Strategic Networks: Information Dissemination and Link Formation Among Self-interested Agents," in IEEE J. Sel. Areas Commun. - Special issue on Network Science, vol. 31, no. 6, pp. 1115-1123, June 2013. [Link]
  98. S. Ren and M. van der Schaar, " Efficient Resource Provisioning and Rate Selection for Stream Mining in a Community Cloud," IEEE Trans. on Multimedia - Special Section on Cloud Computing, vol. 15, no. 4, pp. 723-734, June 2013. [Link]

  99. S. Ren, M. van der Schaar, "Dynamic Scheduling and Pricing in Wireless Cloud Computing,” IEEE Trans. on Mobile Computing, May 2013.
  100. N. Mastronarde and M. van der Schaar, "Joint Physical-Layer and System-Level Power Management for Delay-Sensitive Wireless Communications," IEEE Trans. on Mobile Computing, vol. 12, no. 4, pp. 694-709, Apr. 2013. [Link] (for a more complete version, please see [Link]) (for source code, please see [Link])
  101. O. Habachi, H. Shiang, M. van der Schaar, and Y. Hayel, "Online Learning based Congestion Control for Adaptive Multimedia Transmission," in IEEE Trans. Signal Process., vol. 61, no. 6, pp. 1460-1469, Mar. 2013. [Link]
  102. N. Mastronarde, K. Kanoun, D. Atienza, P. Frossard, and M. van der Schaar, "Markov Decision Process Based Energy-Efficient On-Line Scheduling for Slice-Parallel Video Decoding on Multicore Systems", IEEE Trans. on Multimedia, vol. 15, no. 2, pp. 268-278, Feb. 2013. [Link]
  103. S. Ren, J. Park, and M. van der Schaar, “Entry and Spectrum Sharing Scheme Selection in Femtocell Communications Markets,” IEEE/ACM Transactions on Networking, vol. 21, no. 2, pp. 218-232, Feb. 2013. [Link]

  104. Y. Zhang and M. van der Schaar, "Information Production and Link Formation in Social Computing Systems,” in IEEE J. Sel. Areas Commun. – Special issue on Economics of Communication Networks and Systems, vol. 30, no. 10, pp. 2136-2145, Dec. 2012. [Link]

  105. J. Xu and M. van der Schaar, "Social Norm Design for Information Exchange Systems with Limited Observations," in IEEE J. Sel. Areas Commun. – Special issue on Economics of Communication Networks and Systems, vol 30, no. 11, pp. 2126-2135, Dec. 2012. [Link]

  106. Y. Su and M. van der Schaar, "Structural Solutions for Additively Coupled Sum Constrained Games," in IEEE Trans. Commun., vol. 60, no. 12, pp. 3779-3796, Dec. 2012. [Link]

  107. S. Ren and M. van der Schaar, "Pricing and Investment for Online TV Content Platforms," in IEEE Trans. Multimedia (special issue: “Smart, social and converged TV”), vol. 14, no. 6, pp 1566-1578, Dec. 2012. [Link]

  108. Y. Xiao and M. van der Schaar, "Dynamic Spectrum Sharing Among Repeatedly Interacting Selfish Users With Imperfect Monitoring," in IEEE J. Sel. Areas Commun. – Special issue on Cognitive Radio Networks, vol. 30, no. 10, pp. 1890-1900, Nov. 2012 [Link]
  109. F. Fu and M. van der Schaar, “Structure-Aware Stochastic Control for Transmission Scheduling,” in IEEE Trans. Veh. Tech. vol. 61, no. 9, pp. 3931-3945, Nov. 2012. [Link] (Click here for Tech report)
  110. Y. Xiao, J. Park, and M. van der Schaar, "Repeated Games With Intervention: Theory and Applications in Communications,” IEEE Trans. Commun., vol. 60, no. 10, pp. 3123-3132, Oct. 2012. [Link]
  111. K. T. Phan, J. Park, and M. van der Schaar, "Near-Optimal Deviation-Proof Medium Access Control Designs in Wireless Networks," in IEEE/ACM Trans. Networking, vol. 20, no. 5, pp. 1581-1594, Oct. 2012 [Link]
  112. N. Mastronarde, F. Verde, D. Darsena, A. Scaglione, and M. van der Schaar, “Transmitting Important Bits and Sailing High Radio Waves: A Decentralized Cross-layer Approach to Cooperative Video Transmission,” IEEE J. on Select. Areas in Communications Cooperative Networking -- Challenges and Applications, vol. 30, no. 9, pp. 1597-1604, Oct. 2012. [Link] (Click here for Tech report)
  113. O. Habachi, Y. Hu, M. Van der Schaar, Y. Hayel, and F. Wu."MOS-based Congestion Control for Conversational Services", IEEE Journal Sel. Areas Commun., vol. 30, no. 7, pp. 1225-1236, Aug. 2012. [Link]
  114. H. P. Shiang and M. van der Schaar, "Quality-Centric TCP-Friendly Congestion Control for Multimedia Transmission," IEEE Trans. on Multimedia, vol. 14, no. 3 pp. 896-909, June 2012 [Link]
  115. F. Fu and M. van der Schaar, "Structural Solutions for Dynamic Scheduling in Wireless Multimedia Transmission", IEEE Transactions Circuits Systems for Video Tech., vol. 22, no. 5, pp. 727-739, May 2012. [Link]
  116. K. T. Phan, M van der Schaar, and W. R. Zame, "Congestion, Information, and Secret Information in Flow Networks", IEEE J. Sel. Topics Signal Process., Special issue on Game Theory In Signal Processing, vol. 6, no. 2, pp. 117 - 126, Apr. 2012. [Link]
  117. Y. Xiao, J. Park, and M. van der Schaar, "Intervention in Power Control Games With Selfish Users", IEEE J. Sel. Topics Signal Process., Special issue on Game Theory In Signal Processing, vol. 6, no. 2, pp. 165 - 179, Apr. 2012. [Link]
  118. S. Ren and M. van der Schaar, "Data Demand Dynamics in Communications Markets", IEEE Trans. Signal Process., vol. 60, no. 4, pp. 1986 - 2000, Apr. 2012. [Link]
  119. H. Bobarshad, M. van der Schaar, M. Shikh-Bahaei, "Analytical Modeling for Delay-Sensitive Video over WLAN", IEEE Trans. on Multimedia, vol. 14, no. 2, pp. 401 - 414, Apr. 2012. [Link]
  120. R. Izhak-Ratzin, H. Park, and M. van der Schaar, "Online Learning in BitTorrent Systems", IEEE Trans. on Parallel and Distributed Systems, vol. 23, no. 12, pp. 2280-2288, Mar. 2012. [Link]
  121. Y. Zhang and M. van der Schaar, "Peer-to-Peer Multimedia Sharing based on Social Norms", Elsevier Journal Signal Processing: Image Communication Special Issue on "Advances in video streaming for P2P networks", Feb. 2012. [Link]
  122. J. Park and M. van der Schaar, "The Theory of Intervention Games for Resource Sharing in Wireless Communications", IEEE J. Sel. Areas Commun., vol. 30, no. 1, pp. 165-175, Jan. 2012. [Link] [Click here for more about the intervention framework]
  123. N. Mastronarde and M. van der Schaar, "Fast reinforcement learning for energy-efficient wireless communication," IEEE Trans. on Signal Processing, vol. 59, no. 12, pp. 6262 - 6266, Dec. 2011. [Link]
  124. A. Pant, M. van der Schaar, and P. Gupta, "AppAdapt: Opportunistic Application Adaptation in Presence of Hardware Variation," IEEE Trans. on Very Large Scale Integration Systems, Oct. 2011. [Link]
  125. J. Park and M. van der Schaar, "Cognitive MAC Protocols Using Memory for Distributed Spectrum Sharing under Limited Spectrum Sensing", IEEE Trans. Commun., vol. 59, no. 9, pp. 2627-2637, Sep. 2011. [Link]
  126. Y. Su and M. van der Schaar, "Linearly Coupled Communication Games", IEEE Trans. Commun., vol. 59, no. 9, pp. 2543-2553, Sep. 2011. [Link]
  127. S. Ren and M. van der Schaar, "Pricing and Distributed Power Control in Wireless Relay Networks", IEEE Trans. Signal Process., vol. 59, no. 6, pp. 2913-2926, June 2011. [Link]
  128. H. Park. D. S. Turaga, O. Verscheure, and M. van der Schaar, " Foresighted Tree Configuration Games in Resource Constrained Distributed Stream Mining Sensors" Ad Hoc Networks (Invited Paper to Special Issue: Multimedia Ad Hoc and Sensor Network), vol. 9, no. 4, pp. 497-513, June 2011. [Link]
  129. B. Foo, D. Turaga, O. Verscheure, L. Amini, and M. van der Schaar, "Configuring Trees of Classifiers in Distributed Multimedia Stream Mining Systems," IEEE Trans. on Circuits and Systems for Video Technology, vol. 21, no. 3, pp. 245-258, Mar. 2011. [Link]
  130. J. Park and M. van der Schaar, "Adaptive MAC Protocols Using Memory for Networks with Critical Traffic", IEEE Trans. Signal Processing, vol. 59, no.3, pp. 1269-1279, Mar. 2011. [Link]
  131. Z. Lin and M. van der Schaar, "Autonomic and distributed joint routing and power control for delay-sensitive applications in multi-hop wireless networks", IEEE Trans. Wireless Commun., vol. 10, no. 1, pp. 102-113, Jan. 2011. [Link]
  132. J. Park and M. van der Schaar, "Medium Access Control Protocols With Memory", IEEE/ACM Trans. Networking, vol. 18, no. 6, pp. 1921-1934, Dec. 2010. [Link]
  133. Z. Lin and M. van der Schaar, "MAC Layer Jamming Mitigation Using a Game Augmented by Intervention", EURASIP J. Wireless Commun. Networking, vol. 2010, Nov. 2010. [Link]
  134. B. Foo and M. van der Schaar, "A Distributed Approach for Optimizing Cascaded Classifier Topologies in Real-Time Stream Mining Systems" IEEE Trans. Image Process., vol. 19, no. 11, pp. 3035-3048, Nov. 2010. [Link]
  135. J. Park and M. van der Schaar, "A Game Theoretic Analysis of Incentives in Content Production and Sharing over Peer-to-Peer Networks", IEEE J. Sel. Topics Signal Process., vol. 4, no. 4, pp. 704-717, Aug. 2010. [Link]
  136. H. Bobarshad, M. van der Schaar, M. Shikh-Bahaei, "A Low-Complexity Analytical Modeling for Cross-Layer Adaptive Error Protection in Video over WLAN", IEEE Trans. on Multimedia, vol. 12, no. 5, pp. 427-438, Aug. 2010. [Link]
  137. R. Ducasse, D. Turaga, and M. van der Schaar, "Adaptive Topologic Optimization for Large-Scale Stream Mining" IEEE Journal of Selected Topics in Signal Process. (JSTSP), vol. 4, no. 3, pp. 620-636, June 2010. [Link]
  138. Y. Zhang, F. Fu, and M. van der Schaar, "On-Line Learning and Optimization for Wireless Video Transmission", IEEE Trans. Signal Process., vol. 58, no. 6, pp. 3108-3124, June 2010. [Link]
  139. S. Ren and M. van der Schaar, "Distributed power allocation in multi-user multi-channel cellular relay networks", IEEE Trans. Wireless Commun., vol. 9, no. 6, pp. 1952-1964, June 2010. [Link]
  140. H. P. Shiang and M. van der Schaar, "Online Learning in Autonomic Multi-Hop Wireless Networks for Transmitting Mission-Critical Applications," IEEE J. Sel. Areas Commun., vol. 28, no. 5, pp. 728-741, June 2010. [Link]
  141. Y. Su and M. van der Schaar, "Dynamic Conjectures in Random Access Networks Using Bio-inspired Learning," IEEE J. Sel. Areas Commun., vol. 28, no. 4, pp. 587-601, May 2010. [Link] [Long version]
  142. F. Fu and M. van der Schaar, "A Systematic Framework for Dynamically Optimizing Multi-User Video Transmission," IEEE J. Sel. Areas Commun., vol. 28, no. 3, pp. 308-320, Apr. 2010 [Link] (also featured in the IEEE MMTC R-Letter, Apr. 2011. [Link])
  143. H. P. Shiang and M. van der Schaar, "Information-Constrained Resource Allocation in Multi-Camera Wireless Surveillance Networks," IEEE Trans. on Circuits and Systems for Video Technology, vol. 20, no. 4, pp. 505-517, Apr. 2010. [Link]
  144. H. Park and M. van der Schaar, "Evolution of Resource Reciprocation Strategies in P2P Networks", IEEE Trans. Signal Process., vol. 58, no. 3, pp. 1205-1218, Mar. 2010. [Link]
  145. Z. Cao, B. Foo, L. He, and M. van der Schaar, "Optimality and Improvement of Dynamic Voltage Scaling Algorithms for Multimedia Applications," IEEE Trans. on Circuits and Systems, vol. 57, no. 3, pp. 681-690, Mar. 2010 (received Darlington Best Paper award). [Link]
  146. F. Fu and M. van der Schaar, "Decomposition Principles and Online Learning in Cross-Layer Optimization for Delay-Sensitive Applications", IEEE Trans. Signal Process., vol 58, no. 3, pp. 1401-1415, Feb. 2010. [Link]
  147. H. Park and M. van der Schaar, "Fairness Strategies for Wireless Resource Allocation among Autonomous Multimedia Users," IEEE Trans. on Circuits and Systems for Video Technology, vol. 20, no. 2, pp. 297-309, Feb. 2010. [Link]
  148. O. Nasr, M. van der Schaar, and B. Dahnresrad, "A unique beamforming-based equilibrium in multi-user random access SIMO Networks", IEEE Commun. Letters, vol.14, no. 2, Feb. 2010. [Link]
  149. N. Mastronarde and M. van der Schaar, "Online Reinforcement Learning for Dynamic Multimedia Systems," IEEE Trans. on Image Processing, vol. 19, no. 2, pp. 290-305, Feb. 2010. [Link]
  150. H. Park and M. van der Schaar, "Quality-based Resource Brokerage for Autonomous Networked Multimedia Applications," IEEE Trans. on Circuits and Systems for Video Technology, vol. 19, no. 12, pp. 1781-1792, Dec. 2009. [Link]
  151. H. P. Shiang, W. Tu, and M. van der Schaar, "Predictive Spectrum Access for Multimedia Users over Multi-Channel Wireless Networks," Journal of Communications, Special Issue on Multimedia Communications, Networking and Applications, vol. 4, no. 9, pp. 640-653, Oct. 2009. [Link]
  152. Y. Su and M. van der Schaar, "Conjectural Equilibrium in Multi-user Power Control Games", IEEE Trans. Signal Process., vol. 57, no. 9, pp. 3638-3650, Sep. 2009. [Link]
  153. B. Foo and M. van der Schaar, "Informationally-Decentralized System Resource Management for Multiple Multimedia Tasks," IEEE Trans. on Circuits and Systems for Video Technology, vol. 19, no. 9, pp. 1352-1364, Sep. 2009. [Link]
  154. H. Park and M. van der Schaar, "On the Impact of Bounded Rationality in Peer-to-Peer Networks," IEEE Signal Process. Lett., vol. 16, no. 8, pp. 675-678, Aug. 2009. [Link]
  155. N. Mastronarde and M. van der Schaar, "Designing Autonomous Layered Video Coders," Elsevier Journal Signal Processing: Image Communication - Special Issue on Scalable coded media beyond compression, vol. 24, no. 6, pp. 417-436, July 2009. [Link]
  156. N. Kontorinis, Y. Andreopoulos and M. van der Schaar, "Statistical Framework for Video Decoding Complexity Modeling and Prediction," IEEE Trans. Circuits Syst. Video Technol., vol. 19, no. 7, pp. 1000-1013, July 2009. [Link]
  157. Y. Su and M. van der Schaar, "Minimum Required Learning and Impact of Information Feedback Delay for Cognitive Users," IEEE Trans. Veh. Tech., vol. 58, no. 6, pp. 2825-2834, July 2009. [Link]
  158. B. Foo and M. van der Schaar, "A Rules-based Approach for Configuring Chains of Classifiers in Real-Time Stream Mining Systems," EURASIP Journal on Advances in Signal Processing, vol. 2009, Article ID 975640, 17 pages, July 2009. [Link]
  159. Y. Su and M. van der Schaar, "A New Perspective on Multi-user Power Control Games in Interference Channels", IEEE Trans. Wireless Commun., vol. 8, no. 6, pp. 2910-2919, June 2009. [Link]
  160. H. Park and M. van der Schaar, "Coalition based Resource Negotiation for Multimedia Applications in Informationally Decentralized Networks," IEEE Trans. Multimedia, vol. 11, no. 4, pp. 765-779, Jun. 2009. [Link]
  161. H. P. Shiang and M. van der Schaar, "Feedback-Driven Interactive Learning in Dynamic Wireless Resource Management for Delay Sensitive Users," IEEE Trans. Veh. Tech., vol. 58, no. 4, pp. 2030-2043, May 2009. [Link]
  162. N. Mastronarde and M. van der Schaar, "Towards a general framework for cross-layer decision making in multimedia systems," IEEE Trans. on Circuits and Systems for Video Technology, vol. 19, no. 5, pp. 719-732, May 2009. [Link]
  163. F. Fu and M. van der Schaar, "Learning to Compete for Resources in Wireless Stochastic Games," IEEE Trans. Veh. Tech., vol. 58, no. 4, pp. 1904-1919, May 2009. [Link]
  164. F. Fu and M. van der Schaar, "A New Systematic Framework for Autonomous Cross-Layer Optimization," IEEE Trans. Veh. Tech., vol. 58, no. 4, pp. 1887-1903, May, 2009. [Link]
  165. N. Mastronarde and M. van der Schaar, "Automated bidding for media services at the edge of a content delivery network," IEEE Trans. on Multimedia, vol. 11, no. 3, pp. 543-555, Apr. 2009. [Link]
  166. M. van der Schaar and F. Fu, "Spectrum Access Games and Strategic Learning in Cognitive Radio Networks for Delay-Critical Applications," Proc. of IEEE, Special issue on Cognitive Radio, vol. 97, no. 4, pp. 720-740, Apr. 2009. [Link]
  167. J. Hsu and M. van der Schaar, "Cross layer design and analysis of multi-user wireless video streaming over 802.11e EDCA MAC," IEEE Signal Process. Lett., vol. 16, no.4, pp. 268-271, Apr. 2009. [Link]
  168. Z. Lin and M. van der Schaar, "On the correlated equilibrium selection for two-user channel access games," IEEE Signal Process. Lett., vol. 16, no. 3, pp. 156-159. Mar. 2009. [Link]
  169. H. P. Shiang and M. van der Schaar, "Distributed Resource Management in Multihop Cognitive Radio Networks for Delay Sensitive Transmission," IEEE Trans. Veh. Tech., vol. 58, no. 2, pp. 941-953, Feb. 2009. [Link]
  170. H. Park and M. van der Schaar, "A Framework for Foresighted Resource Reciprocation in P2P Networks," IEEE Trans. Multimedia, vol. 11, no. 1, pp. 101-116, Jan. 2009. [Link]
  171. J. Park and M. van der Schaar, "Stackelberg Contention Games in Multi-User Networks," EURASIP Journal on Advances in Signal Process., Special issue on Game Theory in Signal Processing and Communications, vol. 2009, Article ID 305978, 15 pages, Jan. 2009. [Link]
  172. B. Foo, D. Turaga, O. Verscheure, M. van der Schaar, and L. Amini, "Resource Constrained Stream Mining with Classifier Tree Topologies," IEEE Signal Process. Lett., vol. 15, pp. 761-764, Nov. 2008. [Link]
  173. S. -C. Su and M. van der Schaar, "On the Application of Game-Theoretic Mechanism Design for Resource Allocation in Multimedia Systems," IEEE Trans. Multimedia, vol. 10, no. 6, pp. 1197-1207, Oct. 2008. [Link]
  174. H. Park and M. van der Schaar, "Coalition-based Resource Reciprocation Strategies for P2P Multimedia Broadcasting," IEEE Trans. Broadcast. (Special Issue: Quality Issues in Multimedia Broadcasting), vol. 54, no. 3, pp. 557-567, Sep. 2008. [Link]
  175. E. Akyol and M. van der Schaar, "Compression-Aware Energy Optimization for Video Decoding Systems with Passive Power" IEEE Trans. Circuits Syst. Video Technol. , vol. 18, no. 9, pp. 1300-1306, Sep. 2008. [Link]
  176. C. Shen and M. van der Schaar, "Optimal Resource Allocation for Multimedia Applications over Multiaccess Fading Channels," IEEE Trans. Wireless Commun., vol. 7, no. 9, pp. 3546-3557, Sep. 2008. [Link]
  177. H. P. Shiang and M. van der Schaar, "Queuing-Based Dynamic Channel Selection for Heterogeneous Multimedia Applications over Cognitive Radio Networks," IEEE Trans. Multimedia, Vol. 10, no.5, pp. 896?09, Aug. 2008. [Link]
  178. Y. Su and M. van der Schaar, "Multiuser Multimedia Resource Allocation over Multicarrier Wireless Networks," IEEE Trans. Signal Process., vol. 56, pp. 2102-2116, May 2008 [Link]
  179. N. Mastronarde and M. van der Schaar, "A Bargaining Theoretic Approach to Quality-Fair System Resource Allocation for Multiple Decoding Tasks," IEEE Trans. Circuits Syst. Video Technol., vol. 18, no. 4, pp. 453-466, Apr. 2008. [Link]
  180. B. Foo, Y. Andreopoulos, and M. van der Schaar, "Analytical Rate-Distortion-Complexity Modeling of Wavelet-based Video Coders," IEEE Trans. Signal Process., vol. 56, no. 2, pp. 797-815, Feb. 2008. [Link]
  181. M. van der Schaar, D. S. Turaga, and R. Sood, "Stochastic Optimization for Content Sharing in P2P Systems," IEEE Trans. Multimedia, vol. 10, no. 1, pp. 132-144, Jan. 2008. [Link]
  182. Y. Su and M. van der Schaar, "A Simple Characterization of Strategic Behaviors in Broadcast Channels," IEEE Signal Process. Lett., vol. 15, pp. 37-40, Jan. 2008. [Link]
  183. B. Foo and M. van der Schaar, "A Queuing Theoretic Approach to Processor Power Adaptation for Video Decoding Systems," IEEE Trans. Signal Process., vol. 56, no. 1, pp. 378-392, Jan. 2008. [Link]
  184. Y. Andreopoulos and M. van der Schaar, "Incremental Refinement of Computation for the Discrete Wavelet Transform," IEEE Trans. Signal Process., vol. 56, no. 1, pp. 140-157, Jan. 2008. [Link]
  185. F. Fu, D. Turaga, O. Verscheure, M. van der Schaar, and L. Amini, "Configuring Competing Classifier Chains in Distributed Stream Mining Systems" IEEE Journal of Selected Topics in Signal Process. (JSTSP) , vol. 1, no. 4, pp. 548-563, Dec. 2007. [Link]
  186. X. Tong, Y. Andreopoulos, and M. van der Schaar, "Distortion-driven Video Streaming over Multi-hop Wireless Networks with Path Diversity," IEEE Trans. Mobile Comput., vol. 6, no. 12, Dec. 2007. [Link]
  187. N. Mastronarde and M. van der Schaar, "A Queuing-Theoretic Approach to Task Scheduling and Processor Selection for Video Decoding Applications," IEEE Trans. Multimedia, vol. 9, no. 7, pp. 1493-1507, Nov. 2007. [Link]
  188. E. Akyol and and M. van der Schaar, "Complexity Model Based Proactive Dynamic Voltage Scaling for Video Decoding Systems," IEEE Trans. Multimedia, vol. 9, no. 7, pp. 1475-1492, Nov. 2007. [Link]
  189. Q. Li, Y. Andreopoulos, and M. van der Schaar, "Streaming-Viability Analysis and Packet Scheduling for Video over QoS-enabled Networks," IEEE Trans. Veh. Technol., vol. 56, no. 6, pp. 3533-3549, Nov. 2007. [Link]
  190. H. P. Shiang and M. van der Schaar, "Informationally Decentralized Video Streaming over Multi-hop Wireless Networks," IEEE Trans. Multimedia, vol. 9, no. 6, pp. 1299-1313, Oct. 2007. [Link]
  191. F. Fu, T. M. Stoenescu, and M. van der Schaar, "A Pricing Mechanism for Resource Allocation in Wireless Multimedia Applications," IEEE Journal of Selected Topics in Signal Process., Special Issue on Network-Aware Multimedia Process. and Communications, vol. 1, no. 2, pp. 264-279, Aug. 2007. [Link]
  192. H. Park and M. van der Schaar, "Bargaining Strategies for Networked Multimedia Resource Management," IEEE Trans. Signal Process., vol. 55, no. 7, pp. 3496-3511, Jul. 2007. [Link]
  193. S. Shankar and M. van der Schaar, "Performance Analysis of Video Transmission Over IEEE 802.11a/e WLANs," IEEE Trans. Veh. Technol., vol. 56, no. 4, pp. 2346-2362, July 2007. [Link]
  194. F. Fu and M. van der Schaar, "Noncollaborative Resource Management for Wireless Multimedia Applications Using Mechanism Design," IEEE Trans. Multimedia, vol. 9, no. 4, pp. 851-868, Jun. 2007. [Link]
  195. Y. Andreopoulos and M. van der Schaar, "Adaptive Linear Prediction for Resource Estimation of Video Decoding," IEEE Trans. Circuits Syst. Video Technol., vol. 17, no. 6, pp. 751-764, June 2007. [Link]
  196. Y. Andreopoulos and M. van der Schaar, "Complexity-Constrained Video Bitstream Shaping," IEEE Trans. Signal Process., vol. 55, no. 5, pp. 1967-1974, May 2007. [Link]
  197. H. P. Shiang and M. van der Schaar, "Multi-user video streaming over multi-hop wireless networks: A distributed, cross-layer approach based on priority queuing," IEEE J. Sel. Areas Commun.,vol. 25, no. 4, pp. 770-785, May 2007. [Link]
  198. Bengt J. Borgstrom, M. van der Schaar, and A. Alwan, "Rate Allocation for Noncollaborative Multiuser Speech Communication Systems Based on Bargaining Theory," IEEE Trans. Audio, Speech, and Language Process., vol. 15, no. 4, pp. 1156-1166, May 2007. [Link]
  199. A. Fattahi, F. Fu, M. van der Schaar, and F. Paganini, "Mechanism-Based resource allocation for multimedia transmission over spectrum agile wireless networks," IEEE J. Sel. Areas Commun., vol. 25, no. 3, pp. 601-612, Apr. 2007. [Link]
  200. Y. Andreopoulos and M. van der Schaar, "Generalized Phase Shifting for M-Band Discrete Wavelet Packet Transforms," IEEE Trans. Signal Process., vol. 55, no. 2, pp. 742-747, Feb. 2007. [Link]
  201. N. Mastronarde, D. Turaga, and M. van der Schaar, "Collaborative resource exchanges for peer-to-peer video streaming over wireless mesh networks," IEEE J. Sel. Areas Commun., vol. 25, no. 1, pp. 108-118, Jan. 2007. [Link]
  202. M. van der Schaar and D. Turaga, "Cross-layer Packetization and Retransmission Strategies for Delay-Sensitive Wireless Multimedia Transmission," IEEE Trans. Multimedia, vol. 9, no. 1, pp. 185-197, Jan. 2007. [Link]
  203. Y. Andreopoulos, R. Keralapura, M. van der Schaar, and C. Chuah, "Failure-aware, Open-Loop, Adaptive Video Streaming With Packet-Level Optimized Redundancy," IEEE Trans. Multimedia, vol. 8, no. 6, pp. 1274-1290, Dec. 2006. [Link]
  204. Y. Andreopoulos, N. Mastronarde, and M. van der Schaar, "Cross-layer Optimized Video Streaming Over Wireless Multihop Mesh Networks," IEEE J. Sel. Areas Commun., vol. 24, no. 11, pp. 2104-2115, Nov. 2006. [Link]
  205. M. van der Schaar, D. Turaga, and R. Wong, "Classification-Based System For Cross-Layer Optimized Wireless Video Transmission," IEEE Trans. Multimedia, vol. 8, no. 5, pp. 1082-1095, Oct. 2006. [Link]
  206. C. Tillier, B. Pesquet-Popescu, and M. van der Schaar, "3-band motion-compensated temporal structures for scalable video coding," IEEE Trans. Image Process., vol. 15, no. 9, pp. 2545-2557, Sept. 2006. [Link]
  207. M. Wang and M. van der Schaar, "Operational rate-distortion modeling for wavelet video coders," IEEE Trans. Signal Process., vol. 54, no. 9, pp. 3505-3517, Sept. 2006. [Link]
  208. M. van der Schaar, Y. Andreopoulos, and Z. Hu, "Optimized scalable video streaming over IEEE 802.11 a/e HCCA wireless networks under delay constraints," IEEE Trans. Mobile Comput., vol. 5, no. 6, pp. 755-768, June 2006 [Link]
  209. M. Wang and M. van der Schaar, "Model-based joint source channel coding for subband video," IEEE Signal Process. Lett., vol. 13, no. 6, pp. 341-344, June 2006. [Link]
  210. Y. Wang, M. van der Schaar, S. Chang, and A. Loui, "Classification-based multidimensional adaptation prediction for scalable video coding using subjective quality evaluation," IEEE Trans. Circuits Syst. Video Technol., vol. 15, no. 10, pp. 1270-1279, Oct. 2005. [Link]
  211. D. Turaga, M. van der Schaar, and B. Pesquet-Popescu, "Complexity scalable motion compensated wavelet video encoding," IEEE Trans. Circuits Syst. Video Technol., vol. 15, no. 8, pp. 982-993, Aug. 2005. [Link]
  212. M. van der Schaar and S. Shankar, "Cross-layer wireless multimedia transmission: challenges, principles, and new paradigms," IEEE Wireless Commun. Mag., vol. 12, no. 4, pp. 50-58, Aug. 2005. [Link]
  213. M. van der Schaar and Y. Andreopoulos, "Rate-distortion-complexity modeling for network and receiver aware adaptation," IEEE Trans. Multimedia, vol. 7, no. 3, pp. 471-479, June 2005. [Link]
  214. C. Tiller, B. Pesquet-Popescu, and M. van der Schaar, "Improved update operators for lifting-based motion-compensated temporal filtering," IEEE Signal Process. Lett., vol. 12, no. 2, pp. 146-149, Feb. 2005. [Link]
  215. D. Turaga, M. van der Schaar, Y. Andreopoulos, A. Munteanu, and P. Schelkens, "Unconstrained motion compensated temporal filtering (UMCTF) for efficient and flexible interframe wavelet video," EURASIP Signal Processing: Image Communication, vol. 20, no. 1, pp. 1-19, Jan. 2005. [Link]
  216. J.-R. Ohm, M. van der Schaar, and J. W. Woods, "Interframe wavelet coding x motion picture representation for universal scalability," EURASIP Signal Processing: Image Communication, Special issue on Digital Camera, vol. 19, no. 9, pp. 877-908, Oct. 2004. [Link]
  217. Y. Andreopoulos, A. Munteanu, J. Barbarien, M. van der Schaar, J. Cornelis, and P. Schelkens, "In-band motion compensated temporal filtering," EURASIP Signal Processing: Image Communication, Special issue on Subband/Wavelet Interframe Video Coding, vol. 19, no. 7, pp. 653-673, Aug. 2004. [Link]
  218. Q. Li and M. van der Schaar, "Providing adaptive QoS to layered video over wireless local area networks through real-time retry limit adaptation," IEEE Trans. Multimedia, vol. 6, no. 2, pp. 278-290, Apr. 2004. [Link]
  219. H. Radha, M. van der Schaar, and S. Karande, "Scalable video transcaling for the wireless internet," EURASIP Journal on Applied Signal Process., Special issue on Multimedia over IP and Wireless Networks, vol. 24, no. 2, pp. 265-279, Feb. 2004. [Link]
  220. M. van der Schaar, S. Krishnamachari, S. Choi, and X. Xu, "Adaptive cross-layer protection strategies for robust scalable video transmission over 802.11 WLANs," IEEE J. Sel. Areas Commun., vol. 21, no. 10, pp. 1752-1763, Dec. 2003. [Link]
  221. M. van der Schaar and H. Radha, "Adaptive motion-compensation fine-granular-scalability (AMC-FGS) for wireless video," IEEE Trans. Circuits Syst. Video Technol., vol. 12, no. 6, pp. 360-371, June 2002. [Link]
  222. M. van der Schaar and H. Radha, "Unequal packet loss resilience for fine-granular-scalability video," IEEE Trans. Multimedia, vol. 3, no. 4, pp. 381-394, Dec. 2001. [Link]
  223. M. van der Schaar and H. Radha, "A hybrid temporal-SNR fine-granular scalability for internet video," IEEE Trans. Circuits Syst. Video Technol., vol. 11, no. 3, pp. 318-331, Mar. 2001. [Link]
  224. H. Radha, M. van der Schaar, and Y. Chen, "The MPEG-4 fine-grained scalable video coding method for multimedia streaming over IP," IEEE Trans. Multimedia, vol. 3, no. 1, pp. 53-68, Mar. 2001. [Link]
  225. M. van der Schaar and P. H. de With, "Hybrid compression of video with graphics in DTV communication systems," IEEE Trans. Consumer Electron., vol. 46, no. 4, pp. 1007-1017, Nov. 2000. [Link]
  226. M. van der Schaar and P. H. de With, "Near-lossless complexity-scalable embedded compression algorithm for cost reduction in DTV receivers," IEEE Trans. Consumer Electron., vol. 46, no. 4, pp. 923-933, Nov. 2000. [Link]
  227. P. H. de With, P. H. Frencken, and M. van der Schaar, "An MPEG decoder with embedded compression for memory reduction," IEEE Trans. Consumer Electron., vol. 44, no. 3, pp. 545-555, Aug. 1998. [Link]


    Selected Conference Papers

  1. A. Bellot, M. van der Schaar, "Multitask Boosting for Survival Analysis with Competing Risks," NIPS, 2018.
  2. B. Lim, A. Alaa, M. van der Schaar, "Forecasting Treatment Responses Over Time Using Recurrent Marginal Structural Networks," NIPS, 2018.
  3. J. Pohle, R. King, M. van der Schaar, R. Langrock, "Coupled Markov-switching regression: inference and a case study using electronic health record data," International Workshop on Statistical Modeling (IWSM), 2018. [Link] - Best student paper award
  4. E. Giunchiglia, A. Nemchenko, M. van der Schaar, "RNN-SURV: a Deep Recurrent Model for Survival Analysis," International Conference on Artificial Neural Networks (ICANN), 2018. [Link]
  5. A. Nemchenko, T. Kyono, M. van der Schaar, "Siamese Survival Analysis with Competing Risks," International Conference on Artificial Neural Networks (ICANN), 2018. [Link]
  6. S. Maghsudi, M. van der Schaar, "Distributed Task Management in Cyber-Physical Systems: How to Cooperate under Uncertainty?," IEEE Globecom - Ad Hoc and Sensor Networking Symposium (AHSN), 2018.
  7. A. Bellot, M. van der Schaar, "Boosted Trees for Risk Prognosis," Machine Learning for Healthcare Conference (MLHC), 2018. [Link]
  8. B. Lim, M. van der Schaar, "Disease-Atlas: Navigating Disease Trajectories using Deep Learning," Machine Learning for Healthcare Conference (MLHC), 2018. [Link] [Presentation] - Best Paper Award in IJCAI-BOOM Workshop
  9. J. Jordon, J. Yoon, M. van der Schaar, "Measuring the quality of Synthetic data for use in competitions," 2018 KDD Workshop on Machine Learning for Medicine and Healthcare, 2018. [Link]
  10. B. Lim, M. van der Schaar, "Forecasting Disease Trajectories in Alzheimer's Disease Using Deep Learning," 2018 KDD Workshop on Machine Learning for Medicine and Healthcare, 2018. [Link]
  11. O. Atan, W. R. Zame, M. van der Schaar, "Counterfactual Policy Optimization Using Domain-Adversarial Neural Networks," ICML 2018 Causal Machine Learning Workshop, 2018. [Link]
  12. J. Yoon, J. Jordon, M. van der Schaar, "GAIN: Missing Data Imputation using Generative Adversarial Nets," ICML, 2018. [Link] [Appendix]
  13. J. Yoon, J. Jordon, M. van der Schaar, "RadialGAN: Leveraging multiple datasets to improve target-specific predictive models using Generative Adversarial Networks," ICML, 2018. [Link] [Appendix]
  14. A. M. Alaa, M. van der Schaar, "AutoPrognosis: Automated Clinical Prognostic Modeling via Bayesian Optimization with Structured Kernel Learning," ICML, 2018. [Link]
  15. A. M. Alaa, M. van der Schaar, "Limits of Estimating Heterogeneous Treatment Effects: Guidelines for Practical Algorithm Design," ICML, 2018. [Link]
  16. A. M. Alaa, D. J. Llewellyn, C Routledge, M. van der Schaar, "Mnemosyne: A Decision Support System for Early Detection of Dementia," Submitted, 2018. [Link]
  17. R. Bugiardini, E. Cenko, J. Yoon, B. Ricci, D. Milicic, S. Kedev, Z. Vasiljevic, O. Manfrini, M. van der Schaar, L. Badimon, "Late PCI in STEMI: A Complex Interaction between Delay and Age," American College of the Cardiology (ACC) - 67th Annual Scientific Session & Expo - Orlando; Journal of the American College of Cardiology, 71 (11 Supplement) A44, Mar 2018. [Link]
  18. J. Yoon, J. Jordon, M. van der Schaar, "GANITE: Estimation of Individualized Treatment Effects using Generative Adversarial Nets," ICLR, 2018. [Link]
  19. J. Yoon, W. R. Zame, M. van der Schaar, "Deep Sensing: Active Sensing using Multi-directional Recurrent Neural Networks," ICLR, 2018. [Link]
  20. Y. Zhou, C. Shen, X. Luo, M. van der Schaar, "A Non-Stationary Online Learning Approach to Mobility Management," IEEE ICC 2018 Wireless Networking Symposium, 2018.
  21. A. Bellot, M. van der Schaar, "Tree-based Bayesian Mixture Model for Competing Risks," AISTATS, 2018. [Link]
  22. B. Ricci, M. van der Schaar, J. Yoon, E. Cenko, Z. Vasiljevic, M. Dorobantu, M. Zdravkovic, S. Kedev, O. Kalpak, D. Milicic, O. Manfrini, L. Badimon, R. Bugiardini, "Machine Learning Techniques for Risk Stratification of Non-ST-Elevation Acute Coronary Syndrome: The Role of Diabetes and Age," American Heart Association Scientific Session, 2017 - Circulation, 2017; 136:A15892. [Link]
  23. C. Lee, W. R. Zame, J. Yoon, M. van der Schaar, "DeepHit: A Deep Learning Approach to Survival Analysis with Competing Risks," AAAI, 2018. [Link] [Supplementary Materials]
  24. O. Atan, J. Jordon, M. van der Schaar, "Deep-Treat: Learning Optimal Personalized Treatments from Observational Data using Neural Networks," AAAI, 2018. [Link]
  25. A. M. Alaa, M. van der Schaar, "Deep Multi-task Gaussian Processes for Survival Analysis with Competing Risks," NIPS, 2017. [Link] - Selected as a spotlight paper
  26. A. M. Alaa, M. van der Schaar, "Bayesian Inference of Individualized Treatment Effects using Multi-task Gaussian Processes," NIPS, 2017. [Link] [Supplementary Materials]
  27. K. Ahuja, W. R. Zame, M. van der Schaar, "DPSCREEN: Dynamic Personalized Screening," NIPS, 2017. [Link] [Poster]
  28. J. Yoon, M. van der Schaar, "E-RNN: Entangled Recurrent Neural Networks for Causal Prediction," ICML 2017 Workshop on Principled Approaches to Deep Learning, 2017. [Link]
  29. A. M. Alaa, M. Weisz, M. van der Schaar, "Deep Counterfactual Networks with Propensity-Dropout," ICML 2017 Workshop on Principled Approaches to Deep Learning, 2017. [Link]
  30. J. Yoon, W. R. Zame, M. van der Schaar, "Multi-directional Recurrent Neural Networks: A Novel Method for Estimating Missing Data," ICML 2017 Time Series Workshop, 2017. [Link]
  31. N. Akbarzadeh, C. Tekin, M. van der Schaar, "Online Learning in Limit Order Book Trade Execution," IEEE GlobalSIP Symposium on Signal and Information Processing for Finance and Business, 2017. [Link]
  32. A. M. Alaa, J. Yoon, S. Hu, M. van der Schaar, "Individualized Risk Prognosis for Critical Care Patients: A Multi-task Gaussian Process Model," Big Data in Medicine: Tools, Transformation and Translation, Cambridge, 2017. [Link]
  33. A. M. Alaa, S. Hu, M. van der Schaar, "Learning from Clinical Judgments: Semi-Markov-Modulated Marked Hawkes Processes for Risk Prognosis," ICML, 2017. [Link]
  34. A. Banerjee, J. Yoon, W. R. Zame, M. Cadeiras, A. M. Alaa, M. van der Schaar, "Personalized Risk Prediction using Predictive Pursuit Machine Learning: A Pilot Study in Cardiac Transplantation," European Society of Cardiology Congress, 2017. - Selected as Best Posters in Advanced Heart Failure.
  35. J. Yoon, W. R. Zame, A. Banerjee, M. Cadeiras, A. M. Alaa, M. van der Schaar, "Personalized Risk Prediction using Predictive Pursuit Machine Learning: A Pilot Study in Cardiac Transplantation," Evidence Live Conference, 2017. [Link]
  36. S. Amuru, R. M. Buehrer, M. van set Schaar, "Bandit Strategies for Blindly Attacking Networks," IEEE International Conference on Communications (ICC), 2017.
  37. Z. Wang, C. Shen, X. Luo, M. van set Schaar, "Learn to Adapt: Self-Optimizing Small Cell Transmit Power with Correlated Bandit Learning," IEEE International Conference on Communications (ICC), 2017. [Link]
  38. M. K. Ross, J. Yoon, K. Moon, M. van der Schaar, "A Personalized Approach to Asthma Control Over Time: Discovering Phenotypes Using Machine Learning," American Thoracic Society (ATS) International Conference, 2017. [Link]
  39. C. Shen, M. van der Schaar, "A Learning Approach to Frequent Handover Mitigations in 3GPP Mobility Protocols," IEEE WCNC, 2017.
  40. A. M. Alaa, J. Yoon, S. Hu, M. van der Schaar, "A Semi-Markov Switching Linear Gaussian Model for Censored Physiological Data," NIPS - Workshop on Machine Learnin for Health, 2016. [Link]
  41. J. Yoon, A. M. Alaa, M. Cadeiras, M. van der Schaar, "Personalized Donor-Recipient Matching for Organ Transplantation," AAAI, 2017. [Link] [Poster]
  42. J. Xu, Y. Han, D. Marcu, M. van der Schaar, "Progressive Prediction of Student Performance in College Programs," AAAI, 2017. [Link]
  43. A. M. Alaa and M. van der Schaar, "Balancing Suspense and Surprise: Timely Decision Making with Endogenous Information Acquisition," NIPS, 2016. [Link] [Poster]
  44. W. Hoiles and M. van der Schaar, "A Non-parametric Learning Method for Confidently Estimating Patient's Clinical State and Dynamics ," NIPS, 2016. [Link] [Poster]
  45. H.-S. Lee, C. Tekin, M. van der Schaar, J.-W. Lee, "Contextual Learning for Unit Commitment with Renewable Energy Sources ," GlobalSIP, 2016.
  46. A. M. Alaa, J. Yoon, S. Hu, M. van der Schaar, "Personalized Risk Scoring for Critical Care Patients using Mixtures of Gaussian Process Experts ," ICML 2016 Workshop on Computational Frameworks for Personalization. [Link]
  47. J. Yoon, A. M. Alaa, S. Hu, M. van der Schaar, "ForecastICU: A Prognostic Decision Support System for Timely Prediction of Intensive Care Unit Admission ," ICML, 2016. [Link]
  48. W. Whoiles, M. van der Schaar, "Bounded Off-Policy Evaluation with Missing Data for Course Recommendation and Curriculum Design," ICML, 2016. [Link]
  49. S. Muller, O. Atan, M. van der Schaar, A. Klein, "Smart Caching in Wireless Small Cell Networks via Contextual Multi-Armed Bandits," ICC, 2016.
  50. C. Tekin, J. Yoon, and M. van der Schaar, "Adaptive ensemble learning with confidence bounds for personalized diagnosis ," accepted to appear in AAAI Workshop on Expanding the Boundaries of Health Informatics using AI (HIAI'16):Making Proactive, Personalized, and Participatory Medicine A Reality, 2016. [Link]
  51. S. Li, J. Xu, M. van der Schaar, W. Li, "Popularity-Driven Content Caching ," accepted to appear in Infocom 2016. [Link]
  52. C.K. Yu, M. van der Schaar, A. H. Sayed, "Adaptive Learning for Stochastic Generalized Nash Equilibrium Problems," accepted to appear in ICASSP 2016.
  53. V. Di Valerio, C. Petrioli, L. Pescosolido, M. van der Schaar, "A Reinforcement Learning-based Data-Link Protocol for Underwater Acoustic Communications ," accepted to appear in ACM International Conference on Underwater Networks & Systems 2015 (WUWNet?5). [Link]
  54. Y. Meier, J. Xu, O. Atan, M. van der Schaar, "Personalized Grade Prediction: A Data Mining Approach ," accepted to IEEE ICDM, 2015. [Link]
  55. Y. Xiao, M. van der Schaar, "Optimal Intervention for Incentivizing the Adoption of Commercial Electric Vehicles ," accepted to GlobalSIP, 2015. [Link]
  56. S. Amuru, Y. Xiao, M. van der Schaar, and M. Buehrer, "To Send or Not To Send - Learning MAC Contention ," Globecom 2015. [Link]
  57. K. Sudusinghe, Y. Jiao, H. Ben Salem, M. van der Schaar, and S. S. Bhattacharyya, "Multiobjective Design Optimization in the Lightweight Dataflow for DDDAS Environment LiD4E ," ICCS 2015. [Link]
  58. Z. Yuan, Y. Xue and M. van der Schaar, ""BitMiner: Bits Mining in Internet Traffic Classification," SIGCOMM'15.. [Link]
  59. K. Ahuja, S. Zhang, M. van der Schaar, "The Population Dynamics of Websites," Netecon workshop at ACM Electronic Commerce (EC), 2015..[Link]
  60. E. Soltanmohammadi, M. Naraghi-Pour, M. van der Schaar, "Context-based Unsupervised Data Fusion for Decision Making," ICML, 2015. [Link]
  61. O. Atan, C. Tekin, M. van der Schaar and W. Hsu, "A Data-Driven Approach for Matching Clinical Expertise to Individual Cases," ICASSP, 2015. [Link]
  62. C.Tekin, J. Braun and M. van der Schaar, "eTutor: Online Learning for Personalized Education," ICASSP, 2015. [Link]
  63. S. Barbarossa, P. Di Lorenzo, M. van der Schaar, "Network Formation Games based on Conditional Independence Graphs," ICASSP, 2015.
  64. O. Atan, C. Tekin, M. van der Schaar, "Global Multi-armed Bandits with H?der Continuity," AISTATS, 2015. [Link]
  65. S. Amuru, C. Tekin, M. van der Schaar, M. Buehrer, "A Systematic Learning Method for Optimal Jamming," ICC, 2015. [Link]
  66. J. Xu, D. Sow, D. Turaga and M. van der Schaar, "Online Transfer Learning for Differential Diagnosis Determination," AAAI Workshop on the World Wide Web and Public Health Intelligence, 2015. [Link]
  67. J. Xu, H. Li, J. Liu and M. van der Schaar, "Timely Popularity Forecasting based on Social Networks," IEEE INFOCOM, 2015. [Link]
  68. K. Ahuja, S. Zhang and M. van der Schaar, "Towards a Theory of Societal Co-Evolution: Individualism versus Collectivism," GlobalSIP, 2014. [Link]
  69. C. Tekin and M. van der Schaar, "Discovering, Learning and Exploiting Relevance," Neural Information Processing Systems (NIPS), 2014. [Link]
  70. K. Kanoun and M. van der Schaar, "Big-Data Streaming Applications Scheduling with Online Learning and Concept Drift Detection," DATE 2015. [Link]
  71. J. Xu, D. Deng, U. Demiryurek, C. Shahabi, and M. van der Schaar, "Context-Aware Online Spatiotemporal Traffic Prediction," ICDM Workshop on Spatial and Spatio-Temporal Data Mining, 2014. [Link]
  72. Y. Xiao, F. Dörfler, and M. van der Schaar, "Rating and Matching in Peer Review Systems," Allerton 2014. [Link]
  73. J. Xu, S. Zhang, and M. van der Schaar, "Network Dynamics with Incomplete Information and Learning," Allerton 2014. [Link]
  74. C. Tekin, L. Canzian, and M. van der Schaar, "Context-Adaptive Big Data Stream Mining," Allerton 2014. [Link]
  75. C. Tekin and M. van der Schaar, "An Experts Learning Approach to Mobile Service Offloading," Allerton 2014. [Link]
  76. Y. Xiao and M. van der Schaar, "Decentralized Foresighted Energy Purchase and Procurement With Renewable Generation and Energy Storage,"CDC 2014. [Link]
  77. D. Katselis, C. L. Beck, and M. van der Schaar, "Ensemble Online Clustering through Decentralized Observations," CDC 2014. [Link]
  78. Y. Xiao, K. Ahuja, and M. van der Schaar, "Spectrum Sharing For Delay-Sensitive Applications With Continuing QoS Guarantees," Globecom 2014. [Link]
  79. J. Xu, J. Y. Xu, L. Song, G. Pottie, and M. van der Schaar, "Context-Driven Online Learning for Activity Classification in Wireless Health," Globecom 2014. [Link]
  80. C. Shen, J. Xu, and M. van der Schaar, "Silence is Gold: Strategic Small Cell Interference Management Using Tokens," Globecom 2014. [Link]
  81. K. Sudusinghe, I. Cho, M. van der Schaar, and S. Bhattacharyya, "Model Based Design Environment for Data-Driven Embedded Signal Processing Systems," Procedia Computer Science, ICCS 2014, vol. 29, pp. 1193-1202, 2014. [Link]
  82. K. Kanoun, M. Ruggiero, D. Atienza, and M. van der Schaar, "Low Power and Scalable Many-Core Architecture for Big-Data Stream Computing," ISVLSI 2014.[Link]
  83. M. van der Schaar and S. Zhang, "A Dynamic Model of Certification and Reputation," ACM Conference on Economics and Computation (EC) 2014.[Link]
  84. Y. Song and M. van der Schaar, "Dynamic Network Formation with Incomplete Information," XVI Southwest Economic Theory Conference UC Irvine, March 21-22, 2014.[Link]
  85. B. Kim, Y. Zhang, M. van der Schaar, and J. Lee, "Dynamic Pricing for Smart Grid with Reinforcement Learning," 2014 IEEE INFOCOM Workshop on Communications and Control for Smart Energy Systems.[Link]
  86. Y. Xiao and M. van der Schaar, "Optimal Foresighted Packet Scheduling and Resource Allocation for Multi-user Video Transmission in 4G Cellular Networks," ICASSP 2014. [Link]
  87. L. Canzian and M. van der Schaar, "A Network of Cooperative Learners For Data¨CDriven Stream Mining," ICASSP 2014. [Link]
  88. L. Song, C. Tekin, and M. van der Schaar, "Clustering Based Online Learning in Recommender Systems: A Bandit Approach," ICASSP 2014. [Link]
  89. J. Xu, Y. Song, and M. van der Schaar, "Incentivizing Information Sharing in Networks," ICASSP 2014. [Link]
  90. J. Alcaraz and M. van der Schaar, "Intervention Framework for Counteracting Collusion in Spectrum Leasing Systems," ICASSP 2014. [Link]
  91. L. Song, Y. Xiao, and M. van der Schaar, "Non-stationary Demand Side Management Method for Smart Grids," ICASSP 2014. [Link]
  92. O. Atan, Y. Andreopoulos, C. Tekin, and M. van der Schaar, "Bandit Framework for Systematic Learning in Wireless Video-Based Face Recognition," ICASSP 2014.[Link]
  93. Y. Zhang and M. Van der Schaar, "Structure-aware Stochastic Load Management in Smart Grids," Infocom 2014. [Link]
  94. Y. Xiao and M. Van der Schaar, "Distributed Demand Side Management Among Foresighted Decision Makers in Power Networks," 47th Asilomar Conf. on Signals, Systems, and Computers , 2013. [Link]
  95. K. Kanoun, D. Atienza, N. Mastronarde, and M. van der Schaar, "A Unified Online Directed Acyclic Graph Flow Manager for Multicore Schedulers," ASP-DAC 2014. [Link]
  96. C. K. Yu, M. van der Schaar, and A. H. Sayed, "Distributed Spectrum Sensing in the Presence of Selfish Users," CAMSAP 2013. [Link]
  97. N. Mastronarde, V. Patel, J. Xu, M. van der Schaar, "Learning relaying strategies in cellular D2D Networks with Token-Based Incentives," International Workshop on Emerging Technologies for LTE-Advanced and Beyond-4G, IEEE Globecom 2013. [Link]
  98. E. Choi, S. Song, H. Kim, J. Hong, H. Park, and M. van der Schaar, "Utility-based Server Management Strategy in Cloud Networks," Workshop on Cloud Computing Systems, Networks, and Applications (CCSNA), IEEE Globecom 2013. [Link]
  99. W. Zame, J. Xu , and M. van der Schaar, "Learning Perfect Coordination with Minimal Feedback in Wireless Multi-Access Communications," IEEE Globecom 2013. [Link]
  100. Y. Xiao and M. van der Schaar, "Energy-efficient Nonstationary Power Control in Cognitive Radio Networks," IEEE Globecom 2013. [Link]
  101. M. Alizadeh, Y. Xiao, Anna Scaglione, and M. van der Schaar, "Incentive Design for Direct Load Control Programs," Allerton 2013. [Link]
  102. Y. Xiao and M. van der Schaar, "Spectrum Sharing Policies for Heterogeneous Delay-Sensitive Users: A Novel Design Framework," Allerton 2013. [Link]
  103. Y. Xiao and M. van der Schaar, "Nonstationary Resource Sharing with Imperfect Binary Feedback: An Optimal Design Framework for Cost Minimization," Allerton 2013. [Link]
  104. J. Xu, C. Tekin and M. van der Schaar, "Learning Optimal Classifier Chains for Real-time Big Data Mining," Allerton 2013. [Link]
  105. C. Tekin and M. van der Schaar, "Distributed Online Big Data Classification Using Context Information," Allerton 2013. [Link]
  106. L. Canzian, Y. Xiao, M. Zorzi, and M. van der Schaar, "Game Theoretic Design of MAC Protocols: Pricing and Intervention in Slotted-Aloha," Allerton 2013. [Link]
  107. Y. Zhang and M. van der Schaar, "Strategic Information Dissemination in Endogenous Networks," Allerton 2013. [Link]
  108. J. Xu and M. van der Schaar, "Incentive Design for Heterogeneous User-Generated Content Networks," the W-Pin+NetEcon workshop at SIGMETRICS 2013, Pittsburgh, USA, June 2013. [Link]
  109. S. Won, I. Cho, K. Sudusinghe, J. Xu, Y. Zhang, M. van der Schaar, and S. S. Bhattacharyya, "A design methodology for distributed adaptive stream mining systems," Proc. the International Conference on Computational Science, pp. 2482-2491, Barcelona, Spain, June 2013. [Link]
  110. C.K. Yu, M. van der Schaar, and A.H. Sayed, "Cluster Formation Over Adaptive Networks with Selfish Agents," EUSIPCO 2013, Marrakech, Morocco, Sep. 2013. [Link]
  111. C.K. Yu, M. van der Schaar, and A.H. Sayed, "Reputation Design for Adaptive Networks with Selfish Agents," in Proc. SPAWC 2013, pp. 160-164, Darmstadt, Germany, June 2013. [Link]
  112. N. Mastronarde, K. Kanoun, D. Atienza, and M. van der Schaar, "Markov Decision Process Based Energy-efficient Scheduling for Slice-parallel Video Decoding," in Proc. ICME 2013, San Jose, USA, July 2013. [Link]
  113. O. Habachi, N. Mastronarde, H. Shiang, M. van der Schaar, and Y. Hayel, "A Learning Based Congestion Control for Multimedia Transmission in Wireless Networks," in Proc. ICME 2013, San Jose, USA, July 2013. [Link]
  114. K. Sudusinghe, S. Won, M. van der Schaar and S. Bhattacharyya, "A Novel Framework for Design and Implementation of Adaptive Stream Mining Systems," in Proc. ICME 2013, San Jose, USA, July 2013. [Link]
  115. X. Zhu, C. Lan and M. van der Schaar, "Low-complexity reinforcement learning for delay-sensitive compression in networked video stream mining," in Proc. IEEE ICME, San Jose, USA, July 2013. [Link]
  116. Y. Zhang, D. Sow, D. Turaga and M. van der Schaar, "A Fast Online Learning Algorithm for Distributed Mining of BigData," in the Big Data Analytics workshop at SIGMETRICS 2013. [Link]
  117. K. Phan, T. Le-Ngoc, M, van Schaar, and F. Fu, "Joint Scheduling-Traffic Admission Control: Structural Results and Online Learning Algorithm," in Proc. IEEE ICC 2013 - Wireless Communications Symposium , pp. 5468-5472, Budapest, Hungary, June 2013. [Link]
  118. Y. Zhang and M. van der Schaar, "Strategic Information Dissemination and Link Formation in Social Networks," in Proc. ICASSP 2013, pp. 5268-5272, Vancouver, Canada, May 2013. [Link]
  119. J. Xu, Y. Zhang and M. van der Schaar, "Rating systems for enhanced cyber-security investments," in Proc. ICASSP 2013, pp. 2915-2919, Vancouver, Canada, May 2013. [Link]
  120. S. Ren, C. Lan, and M. van der Schaar "Energy-Efficient Design of Real-Time Stream Mining Systems," in Proc. ICASSP 2013, pp. 3592-3596, Vancouver, Canada, May 2013. [Link]
  121. Y. Xiao, Y. Zhang, and M. van der Schaar, "Socially-Optimal Design of Crowdsourcing Platforms With Reputation Update Errors," in Proc. ICASSP 2013, pp. 5263-5267, Vancouver, Canada, May 2013. [Link]
  122. Kim, S. Ren, M. van der Schaar, and J.-W. Lee, "Tiered Billing Scheme for Residential Load Scheduling with Bidirectional Energy Trading," in Proc. SDP 2013, pp. 363-368, Turin, Italy, April 2013. [Link]
  123. Kim, S. Ren, M. van der Schaar, and J.-W. Lee, "Bidirectional Energy Trading for Residential Load Scheduling and Electric Vehicles,"; in Proc. IEEE INFOCOM 2013, pp. 595-599, Turin, Italy, April 2013. [Link]
  124. S. Ren and M. van der Schaar, "Joint Design of Dynamic Scheduling and Pricing in Wireless Cloud Computing," in Proc. IEEE INFOCOM 2013, pp. 185-189, Turin, Italy, April 2013. [Link]
  125. S. Ren and M. Van der Schaar, “Energy-Efficient Community Cloud for Real-Time Stream Mining,” IEEE CDC 2012. [Link]
  126. S. Parsaeefard, M. Van der Schaar and A. Sharafat, "Mitigating Uncertainty in Stackelberg Games," IEEE CDC 2012. [Link]
  127. J. Xu and M. Van der Schaar, "Sustaining Cooperation in Social Exchange Networks with Incomplete Global Information," IEEE CDC 2012. [Link]
  128. Y. Xiao and M. Van der Schaar, "Distributed Spectrum Sharing Policies for Selfish Users with Imperfect Monitoring Ability," in Proc. 46th Asilomar Conf. on Signals, Systems, and Computers, Nov. 2012. [Link]
  129. Y. Xiao and M. Van der Schaar, “Repeated Resource Sharing Among Selfish Players With Imperfect Binary Feedback,” Allerton 2012. [Link]
  130. Y. Zhang and M. van der Schaar, "Collective Ratings for Online Labor Markets," Allerton 2012. [Link]
  131. J. Xu, M. van der Schaar, and W. Zame, "Token Economy for Online Exchange Systems," AAMAS 2012 (Extended Abstract). [Link]
  132. C.J. Ho, Y. Zhang, J. Wortman Vaughan, and M. van der Schaar, "Towards Social Norm Design for Crowdsourcing Markets," HCOMP 2012. [Link]
  133. C. Wu, M. Gerla, and M. van der Schaar, "Social Norm Incentives for Secure Network Coding in MANETs," NetCod 2012. [Link]
  134. O. Habachi, Y. Hu, M. Van der Schaar, Y. Hayel, and F. Wu."QoE-aware Congestion Control Algorithm for Conversational Services," IEEE ICC 2012. [Link]
  135. J. Xu and M. Van der Schaar, "Designing Incentives for Wireless Relay Networks Using Tokens," WiOpt 2012. [Link]
  136. S. Ren and M. Van der Schaar, "Revenue Maximization in Customer-to-Customer Markets," GameNets 2012. [Link]
  137. J. Xu, W. Zame, and M. Van der Schaar, "Token-Based Incentive Protocol Design for Online Exchange Systems," GameNets 2012. [Link]
  138. S. Ren, J. Park and M. van der Schaar," Maximizing Profit on User Generated Content Platforms with Heterogeneous Participants," IEEE Infocom 2012. [ Link]
  139. Y. Zhang and M. van der Schaar," Reputation-based Incentive Protocols in Crowdsourcing Applications," IEEE Infocom 2012. [Link]
  140. N. Mastronarde and M. van der Schaar, “Reinforcement learning for power management in wireless multimedia communications,” IEEE International Conference on Multimedia & Expo (ICME), July 11-15, 2011 [Link] (Also featured in the IEEE COMSOC MMTC R-Letter, Dec. 2011. [Link]
  141. S. Ren, J. Park and M. van der Schaar, "Profit Maximization on user-generated Online Content Platforms," Allerton 2011. [Link]
  142. Y. Zhang and M. van der Schaar, "Influencing the Long-Term Evolution of Online Communities Using Social Norms," Allerton 2011. [Link]
  143. B. Xie, M. van der Schaar, and R. Wesel, "Minimizing Weighted Sum Finish Time for One-to-Many File Transfer in Peer-to-Peer Networks," Allerton 2011. [Link]
  144. Y. Zhang and M. van der Schaar, "User Adaptation and Long-run Evolution in Online Communities," IEEE CDC 2011. [Link]
  145. K. T. Phan, M. van der Schaar, and W. R. Zame, "Secret Information in Communications Networks," IEEE CDC 2011. [Link]
  146. S. Ren and M. van der Schaar, " Data demand dynamics and profit maximization in communications markets," IEEE CDC 2011. [Link]
  147. N. Mastronarde, F. Verde, D. Darsena, A. Scaglione, and M. van der Schaar, "A decentralized cross-layer approach to cooperative video transmission," IEEE Globecom 2011. Link]
  148. S. Parsaeefard, A. R. Sharafat, and M. van der Schaar, " Robust Equilibria in Additively Coupled Games in Communications Networks," IEEE Globecom 2011. [Link]
  149. Y. Zhang and M. van der Schaar, "Designing Incentives for P2P Multimedia Sharing," IEEE Globecom 2011. [Link]
  150. Y. Xiao, J. Park, and M. van der Schaar, "Design and Analysis of Intervention Mechanism In Power Control Games," IEEE Globecom 2011. [Link]
  151. S. Ren, F. Fu, and M. van der Schaar, "Traffic-Dependent Pricing for Delay-Sensitive Multimedia Networks," IEEE Globecom 2011. [Link]
  152. J. Park and M. van der Schaar, "Designing Incentive Schemes Based on Intervention: The Case of Imperfect Monitoring," GameNets 2011. [Link]
  153. Y. Xiao, W. Zame, and M. van der Schaar, "Technology Choices and Pricing Policies in Wireless Networks," GameNets 2011. [Link]
  154. Y. Zhang, J. Park, and M. van der Schaar, "Designing Social Norm Based Incentive Schemes to Sustain Cooperation in a Large Community," GameNets 2011. [Link]
  155. Y. Su and M. van der Schaar, "Additively Coupled Sum Constrained Games," GameNets 2011. [Link]
  156. Y. Xiao, W. Zame, and M. van der Schaar, "Technology Choices and Pricing Policies in Wireless Networks," IEEE ICC Workshop on Game Theory and Resource Allocation for 4G 2011. [Link]
  157. N. Changuel, N. Mastronarde, M. van der Schaar, B. Sayadi, M. Kieffer, "Adaptive scalable layer filtering process for video scheduling over wireless networks based on mac buffer management," ICASSP 2011. [Link]
  158. H. P. Shiang and M. van der Schaar, "Content-aware TCP-friendly congestion control for multimedia transmission," ICASSP 2011. [Link]
  159. Y. Zhang, J. Park, and M. van der Schaar, "Social norm based incentive mechanisms for peer-to-peer networks," ICASSP 2011. [Link]
  160. Y. Zhang and M. van der Schaar, "Social norm and long-run learning in peer-to-peer networks," ICASSP 2011. [Link]
  161. N. Mastronarde and M. van der Schaar, "Reinforcement learning for energy-efficient wireless transmission," ICASSP 2011. [Link]
  162. R. Izhak-Ratzin, H. Park, and M. van der Schaar, "Reinforcement Learning in BitTorrent Systems," Infocom 2011 (mini conference). [Link]
  163. J. Park and M. van der Schaar, "Incentive Provision Using Intervention," Infocom 2011 (mini conference). [Link]
  164. S. Ren, J. Park, and M. van der Schaar, "User Subscription Dynamics and Revenue Maximization in Communication Markets," Infocom 2011. [Link]
  165. Y. Su and M. van der Schaar, "Linearly Coupled Communication Games," Allerton 2010. [Link]
  166. J. Park and M. van der Schaar, "Content Pricing in Peer-to-Peer Networks," NetEcon 2010. [Link]
  167. S. Ren, J. Park, and M. van der Schaar, "Subscription Dynamics and Competition in Communication Markets," NetEcon 2010. [Link]
  168. Y. Su and M. van der Schaar, "Towards Efficient, Stable, and Fair Random Access Networks: A Conjectural Equilibrium Approach," Proc. IEEE Globecom 2010. [Link]
  169. K. T. Phan, J. Park, and M. van der Schaar, "Design and Analysis of Defection-Proof MAC Protocols Using a Repeated Game Framework," Proc. IEEE Globecom 2010. [Link]
  170. S. Ren, J. Park, and M. van der Schaar, "User Subscription Dynamics in Communication Markets," Proc. IEEE Globecom 2010. [Link]
  171. R. Ducasse, D. Turaga, and M. van der Schaar, "Ordering of Stream Mining Classifiers," in Proc. ICIP 2010, Hong Kong. [Link]
  172. A. Pant, P. Gupta, and M. van der Schaar, "Software Adaptation in Quality Sensitive Applications to Deal with Hardware Variability," in Proc. Great Lakes Symposium on VLSI 2010, Providence, Rhode Island, USA, pp. 85-90. [Link]
  173. J. Park and M. van der Schaar, "Pricing and Incentives in Peer-to-Peer Networks," in Proc. IEEE INFOCOM 2010, San Diego, CA, Mar. 2010. [Link]
  174. S. Ren and M. van der Schaar, "Pricing and Distributed Power Control for Relay Networks," in Proc. IEEE ICC 2010. [Link]
  175. N. Mastronarde, M. van der Schaar, A. Scaglione, F. Verde, and D. Darsena, "Sailing good radio waves and transmitting important bits: relay cooperation in wireless video transmission," ICASSP 2010. [Link]
  176. N. Mastronarde and M. van der Schaar, "A new approach to cross-layer optimization of multimedia systems," ICASSP 2010. [Link]
  177. N. Mastronarde and M. van der Schaar, "Online reinforcement learning for multimedia buffer control," ICASSP 2010. [Link]
  178. F. Fu and M. van der Schaar, "Dependent optimal stopping framework for wireless multimedia transmission," ICASSP 2010. [Link]
  179. S. J. Kang, Y. J. Won, S. O. Lim, and M. van der Schaar, "Efficient Resource Management with Reduced Overhead Information," in Proc. PIMRC 2009. [Link]
  180. S. Ren and M. van der Schaar, "Revenue Maximization and Distributed Power Allocation in Cognitive Radio Networks," (invited paper) in Proc. CoRoNet '09, Beijing, China, Sept. 2009. [Link]
  181. R. Ducasse, D. S. Turaga, and M. van der Schaar, "Topology Selection for Stream Mining Systems," in Proc. ACM LS-MMRM'09, pp. 113-120, 2009. [Link]
  182. D. S. Turaga, R. Yan, O. Verscheure, B. Foo, F. Fu, H. Park, and M. van der Schaar, "Resource-adaptive Multimedia Analysis on Stream Mining Systems," (invited paper) in Proc. IEEE ICME '09. [Link]
  183. Y. Su and M. van der Schaar, "Conjectural Equilibrium in Water-filling Games," in Proc. IEEE Globecom '09. [Link]
  184. F. Fu and M. van der Schaar, "Cross-Layer Optimization with Complete and Incomplete Knowledge for Delay-Sensitive Applications," in Proc. Int. Packet Video Workshop 2009 (PV 2009). [Link]
  185. Y. Zhang, F. Fu, and M. van der Schaar, "Online Learning for Wireless Video Transmission with Limited Information," in Proc. Int. Packet Video Workshop 2009 (PV 2009). [Link]
  186. S. I. Lee, H. Park, and M. van der Schaar, "Foresighted Joint Resource Reciprocation and Scheduling Strategies for Real-time Video Streaming over Peer-to-Peer Networks," in Proc. Int. Packet Video Workshop 2009 (PV 2009). [Link]
  187. H. P. Shiang and M. van der Schaar, "Conjecture-Based Channel Selection Game for Delay-Sensitive Users in Multi-Channel Wireless Networks," in Proc. IEEE Gamenets '09. [Link]
  188. J. Park and M. van der Schaar, "Achieving Coordination in Random Access Networks without Explicit Message Passing," in Proc. IEEE Gamenets '09. [Link]
  189. Y. Su and M. van der Schaar, "From Competition to Coopetition: Stackelberg Equilibrium in Multi-user Power Control Games," in Proc. IEEE Gamenets '09, May 2009, pp. 107-116. [Link]
  190. H. Park, Deepak S. Turaga, Olivier Verscheure, and M. van der Schaar, "A Framework for Distributed Multimedia Systems using Coalition-based Foresighted Strategies," in Proc. IEEE Int. Conf. Acoust., Speech, and Signal Process. 2009 (ICASSP '09). [Link]
  191. H. Park, Deepak S. Turaga, Olivier Verscheure, and M. van der Schaar, "Tree Configuration Games for Distributed Stream Mining Systems," in Proc. IEEE Int. Conf. Acoust., Speech, and Signal Process. 2009 (ICASSP '09). [Link]
  192. H. Park and M. van der Schaar, "Evolution of Social P2P Networks based on the Dynamics of Heterogeneous Multimedia Peers," in Proc. IEEE Int. Conf. Acoust., Speech, and Signal Process. 2009 (ICASSP '09) (invited paper to Special Session: Multimedia Social Networks). [Link]
  193. H. P. Shiang and Mihaela van der Schaar, "Delay-Sensitive Resource Management in Multi-Hop Cognitive Radio Networks," in Proc. IEEE Dyspan 2008. [Link]
  194. Ulrich Berthold, Fangwen Fu, Mihaela van der Schaar, and Friedrich K. Jondral, "Detection of Spectral Resources in Cognitive Radios Using Reinforcement Learning," in Proc. IEEE Dyspan 2008 . [Link]
  195. F. Fu and Mihaela van der Schaar, "Stochastic Game Formulation for Cognitive Radio Networks," in Proc. IEEE Dyspan 2008. [Link]
  196. Y. Su and Mihaela van der Schaar, "Learning for Cognitive Wireless Users'," in Proc. IEEE Dyspan 2008. [Link]
  197. H. Park and Mihaela van der Schaar, "Foresighted Resource Reciprocation Strategies in P2P Networks," in Proc. IEEE Globecom., Dec. 2008. [Link]
  198. W. Tu and Mihaela van der Schaar, "Distributed Spectrum Allocation of Delay-sensitive Users over Multi-user Multi-carrier Networks," in Proc. IEEE Globecom., Dec. 2008. [Link]
  199. F. Fu and Mihaela van der Schaar, "A New Theoretic Framework for Cross-layer Optimization," in Proc. IEEE Int. Conf. on Image Process. 2008 (ICIP 2008). [Link]
  200. N. Mastronarde and Mihaela van der Schaar, "A Scalable Complexity Specification for Video Applications," in Proc. IEEE Int. Conf. on Image Process. 2008 (ICIP 2008). [Link]
  201. H. P. Shiang and Mihaela van der Schaar, "Dynamic Channel Selection for Multi-user Video Streaming over Cognitive Radio Networks," in Proc. IEEE Int. Conf. on Image Process. 2008 (ICIP 2008). [Link]
  202. H. Park and Mihaela van der Schaar, "Information-driven Resource Negotiation Strategies for Multimedia Applications," in Proc. IEEE Int. Conf. on Image Process. 2008 (ICIP 2008). [Link]
  203. Z. Cao, B. Foo, L. He, and M. van der Schaar, "Optimality and Improvement of Dynamic Voltage Scaling Algorithms for Multimedia Applications," in Proc. ACM/IEEE Conf. on Design Automation (DAC' 08) (Nominated for best paper award). [Link]
  204. Yi Su and Mihaela van der Schaar, "How Much Learning is Sufficient in Interference Games?," in Proc. Cognitive Info. Process. (CIP 2008). [Link]
  205. Fangwen Fu and Mihaela van der Schaar, "Learning for cross-layer optimization," in Proc. Cognitive Info. Process. (CIP 2008). [Link]
  206. H. P. Shiang, W. Tu, and M. van der Schaar, "Dynamic Resource Allocation of Delay Sensitive Users Using Interactive Learning over Multi-carrier Networks," in Proc. Int. Conf. Commun. 2008 (ICC 2008). [Link]
  207. Y. Su and M. van der Schaar, "Resource Allocation for Multi-user Video Transmission over Multi-carrier Networks," in Proc. Int. Conf. Commun. 2008 (ICC 2008). [Link]
  208. Y. Su and M. van der Schaar, "A New Look at Multi-user Power Control Games," in Proc. Int. Conf. Commun. 2008 (ICC 2008). [Link]
  209. F. Fu and M. van der Schaar, "Dynamic Spectrum Sharing Using Learning for Delay-Sensitive Applications," in Proc. Int. Conf. Commun. 2008 (ICC 2008). [Link]
  210. F. Fu and Mihaela van der Schaar, "A New Theoretic Framework for Cross-Layer Optimization with Message Exchanges," in INFOCOM 2008 Student Workshop. [Link]
  211. H. P. Shiang and M. van der Schaar, "Risk-aware scheduling for multi-user video streaming over wireless multi-hop networks," in Proc. IS&T/SPIE Visual Communications and Image Processing 2008 (VCIP 2008). [Link]
  212. B. Foo and M. van der Schaar, "Distributed Classifier Chain Optimization for Real-time Multimedia Stream Mining Systems," in Proc. IS&T/SPIE Multimedia Content Access, Algorithms and Systems II, Jan. 2008. [Link]
  213. B. Foo and M. van der Schaar, "Joint Scheduling and Resource Allocation for Multiple Video Decoding Tasks," in Proc. IS&T/SPIE Multimedia Communications and Networking 2008 (MCN 2008), Jan. 2008. [Link]
  214. H. Park and M. van der Schaar, "Coalition based Multimedia Peer Matching Strategies for P2P Networks," in Proc. IS&T/SPIE Visual Communications and Image Processing 2008 (VCIP 2008), vol. 6822, Jan. 2008. [Link]
  215. F. Fu and M. van der Schaar, "Resource Management Framework for Multi-user Wireless Multimedia Using the VCG Mechanism," in Proc. 16th Int. Packet Video Workshop 2007 (PV 2007), Nov. 2007, pp. 356-362. [Link]
  216. H. Park and M. van der Schaar, "Congestion game modeling for brokerage based multimedia resource management," in Proc. 16th Int. Packet Video Workshop 2007 (PV 2007), Nov. 2007, pp. 18-25. [Link]
  217. C. Shen and M. van der Schaar, "Optimal Resource Allocation in Wireless Multiaccess Video Transmissions," in Proc. IEEE International Conference on Communications 2007 (ICC 07), June 2007. [Link]
  218. B. Foo and M. van der Schaar, "Graceful quality degradation for video decoding systems through priority scheduling and processor power adaptation," in Proc. IEEE Int. Conf. Image Process., 2007 (ICIP 07). [Link]
  219. B. Foo, Y. Andreopoulos, and M. van der Schaar, "Analytical Complexity Modeling of Wavelet-based Video Coders," in Proc. IEEE Int. Conf. Acoust., Speech and Signal Process., 2007 (ICASSP 07), vol. 3, Apr. 2007, pp. 789-792. [Link]
  220. F. Fu and D. S. Turaga and O. Verscheure and M. van der Schaar and L. Amini, "Configuring Networked Classifiers in Distributed and Resource Constrained Stream Processing Systems," in Proc. IEEE Int. Conf. Acoust., Speech, and Signal Process., 2007 (ICASSP 07), Apr. 2007, pp. 1085-1088. [Link]
  221. H. Park and M. van der Schaar, "Fairness Strategies for Multi-user Multimedia Applications in Competitive Environments using the Kalai-Smorodinsky Bargaining Solution," in Proc. IEEE Int. Conf. Acoust., Speech, and Signal Process. 2007 (ICASSP 07), vol. 2, Apr. 2007, pp. 713-716. [Link]
  222. H. Park and M. van der Schaar, "Multi-User Multimedia Resource Management using Nash Bargaining Solution," in Proc. 2007 IEEE Int. Conf. Acous., Speech, and Signal Process. 2007 (ICASSP 07), vol. 2, Apr. 2007, pp. 717-720. [Link]
  223. Y. Andreopoulos, M. van der Schaar, Z. Hu, S. Heo, S. Suh, "Scalable Resource Management for Video Streaming Over IEEE802.11A/E," in Proc. IEEE Int. Conf. Acoust., Speech and Signal Process. 2006 (ICASSP 06), vol. 5, 2006, pp. 361-364. [Link]
  224. F. Fu, A. R. Fattahi, and M. van der Schaar, "Game-Theoretic Paradigm for Resource Management in Spectrum Agile Wireless Networks," in Proc. 2006 IEEE Int. Conf. Multimedia & Expo (ICME 06), 2006, pp. 873-876. [Link]
  225. N. Mastronarde, Y. Andreopoulos, M. van der Schaar, D. Krishnaswamy, and J. Vicente, "Cross-layer Video Streaming Over 802.11e-Enabled Wireless Mesh Networks," in Proc. IEEE Int. Conf. Acoust., Speech and Signal Process. 2006 (ICASSP 06), vol. 5, 2006, pp. 433-436. [Link]
  226. N. Mastronarde, D. S. Turaga, and M. van der Schaar, "Collaborative Resource Management for Video Over Wireless Multi-Hop Mesh Networks," in Proc. IEEE Int. Conf. Image Process. 2006 (ICIP 06), 2006, pp. 1297-1300. [Link]
  227. H. P. Shiang, D. Krishnaswamy, and M. van der Schaar, "Quality-aware Video Streaming over Wireless Mesh Networks with Optimal Dynamic Routing and Time Allocation," in Proc. 40th Asilomar Conf. on Signals, Systems, and Computers, Oct. 2006. [Link]
  228. H. P. Shiang and M. van der Schaar, "Multi-user Video Streaming over Multi-hop Wireless Networks: A Cross-layer Priority Queuing Scheme," in Proc. IEEE Conf. on Intelligent Info. Hiding and Multimedia Signal Process. (IIH-MSP 2006), Dec. 2006, pp. 255-258. [Link]
  229. R. Sood and M. van der Schaar, "Optimal media sharing policies in peer-to-peer networks," in Proc. SPIE, Nov. 2004, pp. 435-443. [Link]
  230. A. Larcher, H. Sun, M. van der Schaar, and Z. Ding, "Decentralized Transmission Strategy for Delay-Sensitive Applications over Spectrum Agile Network," in Proc. 13th Int. Packet Video Workshop 2004 (PV 2004).(First paper on cognitive radios and dynamic spectrum access games) [Link]


    Chapters

  1. C. Tekin, S. Zhang, J. Xu, and M. van der Schaar, "Multi-agent systems: Learning, strategic behavior, cooperation, and network formation," Cooperative and Graph Signal Processing, Elsevier, 2018.
  2. C. Tekin, and M. van der Schaar, "Actionable intelligence and online learning for semantic computing," Encyclopedia with Semantic Computing and Robotic Intelligence, 2017. [Link]
  3. Y. Xiao and M. van der Schaar, "Cognitive Radio Networks for Delay-Sensitive Applications: Games and Learning," Handbook of Cognitive Radio, Ed. W. Zhang, Springer, 2017. [Link]
  4. D. S. Turaga and M. van der Schaar, "Distributed Online Learning and Stream Processing for a Smarter Planet," Fog Networking, Ed. M. Chiang, F. Bonomi and B. Balasubramanian, Wiley, 2016. [Link]
  5. Y. Xiao and M. van der Schaar, "Optimal Repeated Spectrum Sharing by Delay-Sensitive Users," Cloud Radio Access Networks: Principles, Technologies, and Applications, Ed. T. Quek, M. Peng, O. Simeone and W. Yu, Cambridge University Press, 2016. [Link]
  6. S. Bhattacharyya, M. van der Schaar, O. Atan, C. Tekin, and K. Sudusinghe, "Data-driven Stream Mining Systems for Computer Vision," Advances in Embedded Computer Vision, Ed. B. Kisacanin, M. Gelautz, Margrit, Springer, 2014. [Link]

  7. S. Ren and M. van der Schaar, "To Tax or To Subsidize: The Economics of User-Generated Content Platforms," Smart Data Pricing, John Wiley & Sons, Ed. S. Sen, C. J. Wong, S. Ha, and M. Chiang, 2014. [Link]
  8. R. Ducasse and M. van der Schaar, "Finding It Now: Construction and Configuration of Networked Classifiers in Real-Time," Handbook of Signal Processing Systems, Stream Mining Systems, Springer New York, Ed. S. S. Bhattacharyya, F. Deprettere, R. Leupers and J. Takala, 2013. [Link]
  9. H. Park, R. Izhak Ratzin, and M. van der Schaar, "Peer-to-Peer Networks - Protocols, Cooperation and Competition," Streaming Media Architectures, Techniques, and Applications: Recent Advances, IGI Global, Ed. Ce Zhu, Yuenan Li and Xiamu Niu, 2010. [Link]
  10. H. P. Shiang and M. van der Schaar, "Multi-User Multimedia Transmission over Cognitive Radio Networks Using Priority Queuing," Cognitive Radio Systems, INTECH, Wei Wang (Ed.), ISBN: 978-953-307-021-6, 2009. [Link]

Technical Reports


Miscellaneous

3. N. Mastronarde and M. van der Schaar, “Energy-efficient Delay-critical Communication in Unknown Wireless Environments,” IEEE COMSOC MMTC E-Letter, vol. 7, no. 8, pp. 8-11, Nov. 2012.

2. J. Park and M. van der Schaar, "A Note on the Intervention Framework". [Link]
1. F. Fu and M. van der Schaar, "Separation Principles for Multimedia Delivery over Energy Efficient Networks," IEEE ComSoc MMTC E-letter, Sept. 2010. [Link]


Theses

Yuanzhang Xiao (Graduation Date: May 2014),
Title: Optimal Sequential Resource Sharing and Exchange in Multi-Agent Systems [Link] [Slides]

Abstract:
Central to the design of many engineering systems and social networks is to solve the underlying resource sharing and exchange problems, in which multiple decentralized agents make sequential decisions over time to optimize some long-term performance metrics. It is challenging for the decentralized agents to make optimal sequential decisions because of the complicated coupling among the agents and across time. In this dissertation, we mainly focus on three important classes of multi-agent sequential resource sharing and exchange problems and derive optimal solutions to them.

First, we study multi-agent resource sharing with imperfect monitoring, in which self-interested agents have imperfect monitoring of the resource usage and inflict strong negative externality (i.e. strong interference and congestion) among each other. Despite of the imperfect monitoring, the strong negative externality, and the self-interested agents, we propose an optimal, distributed, easy-to-implement resource sharing policy that achieves Pareto optimal outcomes at the equilibrium. A key feature of the optimal resource sharing policy is that it is nonstationary, namely it makes decisions based on the history of past (imperfect) monitoring of the resource usages. The applications of our proposed design in wireless spectrum sharing problems enable us to improve the spectrum e fficiency by up to 200% and achieve up to 90% energy saving, compared to state-of-the-art (stationary) spectrum sharing policies.

Second, we study multi-agent resource sharing with decentralized information, in which each agent has a private, independently and stochastically changing state (whose transition may depend on the agent's action), and the agents' actions are coupled through resource sharing constraints. Despite of the decentralized information (i.e. private states), we propose distributed resource sharing policies that achieve the social optimum, and apply the proposed policies to demand-side management in smart grids, and joint resource allocation and packet scheduling in wireless video transmissions. The proposed policies demonstrate significant performance gains over existing myopic policies that do not take into account the state dynamics and the policies based on Lyapunov optimization that were proposed for single-agent problems.

Finally, we study multi-agent resource exchange with imperfect monitoring, in which self-interested, anonymous agents exchange services (e.g. task solving in crowdsourcing platforms, file sharing in peer-to-peer networks, answering in question-and-answer forums). Due to the anonymity of the agents and the lack of fixed partners, free-riding is prevalent, and can be addressed by rating protocols. We propose the first rating protocol that can achieve the social optimum at the equilibrium under imperfect monitoring of the service quality. A key feature of the optimal rating protocol is again that it is nonstationary, namely it recommends desirable behaviors based on the history of past rating distributions of the agents.

Yu Zhang (Graduation Date: May 2013),
Title: System and Incentive Design in Socio-technical Networks [Link] [Slides]

Abstract:
Socio-technical networks (e.g. social networking services, peer-to-peer systems, etc.) provide a popular, cost-effective and scalable framework for sharing user-generated resources or services. Achieving resource sharing efficiency in socio-technical networks is a challenging problem, because the information available about the various resources is decentralized and it is changing dynamically; the agents may be heterogeneous and have different learning abilities; the agents may make proactive decisions on link formation; and most importantly, the agents may be self-interested, i.e. they take actions which maximize their individual utilities rather than the collective social welfare and thus choose to free-ride rather than share their resources..

The overarching goal of my dissertation is to develop a rigorous and unified paradigm for the joint design of efficient incentive mechanisms and resource management schemes in socio-technical networks. It can be generally divided into two parts.

The first part focuses on the efficient resource sharing in socio-technical networks. Existing distributed network optimization techniques that enable efficient resource allocation when agents are obedient or cooperative are no longer suitable in socio-technical networks which are formed by self-interested agents. The strategic interactions of such self-interested agents lead in numerous socio-technical networks to (Nash) equilibria that are highly inefficient from a social perspective. To achieve social efficiency, incentives need to be provided to agents such that they find in their own self-interest to cooperate and thus act in a socially-optimal way. I propose a general methodology for the design and analysis of rating protocols and associated multi-agent learning algorithms to sustain cooperation in socio-technical networks. Under a rating protocol, an agent is rated based on its behavior. The rating affects the agent's rewards received in the network, which are typically determined according to a differential resource management scheme: compliant agents receive higher ratings and are rewarded by gaining more access to resources compared to non-compliant agents. This preferential treatment thus provides an incentive for agents to cooperate. I rigorously formalize and study the design of rating protocols to optimize the social resource sharing efficiency while encompassing various unique features of socio-technical networks, including the anonymity of agents, asymmetry of interests between different parties in the network, imperfect monitoring, dynamics in the agent population, and white-washing effects (i.e., an individual agent creating multiple identities in the network).

Different from the first part where the underlying network topology is exogenously determined, the second part of my dissertation augments the proposed rating protocols by investigating the endogenous formation of network topologies by the strategic, self-interested agents who produce, disseminate or collect resources. I propose a novel game-theoretic framework to model and analyze the trade-offs (of each individual agent) between the costs and benefits of producing resources personally and forming links to acquire and disseminate resources. A central point of my analysis, which departs from the existing literature on social network formation, is the assumption that the strategic agents are heterogeneous and that agents value this heterogeneity. The heterogeneity of agents and the ability of agents to strategically produce, disseminate or collect resources have striking consequences on the endogenously emerging topology, which provide important guidelines for the design of effective incentive mechanisms and resource management schemes in endogenous socio-technical networks. I first show that the network topology that emerges (at equilibrium) necessarily displays a core-periphery type: hub agents (at the core of the network) produce most of the resources and also create and maintain links to the agents at the periphery, while spoke agents (at the periphery of the network) derive most of their resources from hub agents, producing little of it themselves. As the population becomes larger, the number of hub agents and the total amount of resources produced grow in proportion to the total population. I then show that the networks that emerge at equilibrium are frequently minimally connected and have short network diameters. These ``scale-free'' conclusions had been conjectured for many networks, such as the ``small-world'' phenomenon in the World-Wide-Web, but not derived in any formal framework, and are in stark contradiction to the ``law of the few'' that had been established in previous work, under the assumption that agents solely benefit by forming links for resource acquisition, while resources are homogeneous and part of the endowment of agents, rather than heterogeneous and produced.

Brian Foo (Graduation Date: May 2008),
Title: Towards a Systematic Approach for Modeling and Optimizing Distributed and Dynamic Multimedia Systems [Link] [Slides]

Abstract:
Recent advances in low-power, multi-core and distributed computing technologies have opened up exciting research opportunities, as well as unique challenges, for modeling, designing, and optimizing multimedia systems and applications. First, multimedia applications are highly dynamic, with source characteristics and workloads that can change significantly within milliseconds. Hence, systems need to be able to optimally adapt their scheduling, resource allocation, and resource adaptation strategies on-the-fly to meet the multimedia applications' time-varying resource demands within the delay constraints specified by each application. Second, systems often need to support multiple concurrent multimedia applications and thus, (Pareto) efficient and fair resource management solutions for dividing processing resources among the competing applications need to be designed. Finally, some applications require distributed computing resources or processing elements, which are located across different autonomous sites. These different sites can collaborate in order to jointly process the multimedia data by exchanging information about their specific system implementations, algorithms and processing capabilities. However, exchanging this information among these autonomous entities may result in unacceptable delays or transmission overheads. Moreover, they may even refuse to share this information due to proprietary or legal restrictions. Thus, information-decentralization can present a major obstacle for optimizing the performance of delay-sensitive multimedia applications that require coordination and cooperation between distributed, autonomous sites.

This dissertation addresses the above challenges by providing a systematic framework for modeling and optimizing multimedia systems in dynamic, resource-constrained, and informationally-distributed environments. In particular, we propose a stochastic modeling approach to capture the dynamically changing utilities and workload variations inherent in multimedia applications. This approach enables us to determine analytical solutions for optimizing the performance of applications on resource-constrained systems. Furthermore, the problem of information-decentralization can be addressed in our framework by systematically decomposing the joint multi-applications and multi-site optimization problems, and designing corresponding mechanisms for exchanging model parameters, which characterize the utilities, constraints and features of the autonomous entities. This systematic decomposition enables entities to autonomously coordinate and collaborate under informational and delay constraints. Finally, to optimize the performance of the multimedia applications or systems in these distributed environments, we deploy multi-agent learning strategies, which enable individual sites or applications to model the behaviors of its competitors or peers and, based on this, select their optimal parameters, configurations, and algorithms in an autonomous manner. Summarizing, our framework proposes a unified approach combining stochastic modeling, systematic information exchange mechanisms, and interactive learning solutions for optimizing the performance of a wide range of multimedia systems.

A unique and distinguishing feature of our approach is the extent of multimedia algorithms and systems domain specific knowledge used in developing the proposed framework for modeling, and optimizing the interacting system components and applications. This is in contrast to existing distributed optimization or game theoretic approaches, which use simplistic utility - resource functions, and often ignore the dynamics and constraints experienced in actual multimedia systems. Instead, our developed modeling and optimization framework is directly shaped by the specific characteristics, constraints and requirements of multimedia systems. Specifically, the proposed framework provides pragmatic implementation solutions for (i) the optimization of dynamic voltage scaling algorithms for multimedia applications, (ii) energy-aware resource management for multiple multimedia tasks, and (iii) resource-constrained adaptation for cascaded classifier topologies in distributed stream mining systems.

Hyunggon Park (Graduation Date: Nov. 2008),
Title: Distributed and Dynamic Resource Management Strategies for Multimedia Networks using Cooperative Game Theoretic Approaches [Link] [Slides]

Abstract:
This thesis proposes a distributed and dynamic multi-user resource management framework, which enables heterogeneous multimedia users that repeatedly interact in a dynamically varying network environment to strategically maximize their own utilities, given their private information. For this, we rely on cooperative game-theoretic concepts. For instance, we model the bilateral interactions among users as resource reciprocation games. Using our formulation, the resource reciprocation among the various peers in a peer-to-peer network is modeled as a stochastic game. Within this game, the peers can autonomously determine their optimal strategies for resource reciprocation using a Markov Decision Process (MDP) formulation. Unlike existing myopic solutions for resource reciprocation such as Tit-For-Tat, the optimal strategies determined based on MDP enable the peers to make foresighted decisions about resource reciprocation, such that they can explicitly consider both their immediate and future expected utilities. In the resource reciprocation games, the participating users need to mutually agree on a particular resource division. For this, we propose a methodology for designing utility-aware resource division solutions which are able to fulfill desired fairness axioms in terms of multimedia performance. We also show how the proposed resource management framework can also be successfully deployed in cognitive radio networks, wireless multimedia broadcasting, distributed stream mining systems, etc.

Hsien-Po Shiang (Graduation Date: Mar. 2009),
Title: Designing Autonomic Wireless Multi-Hop Networks for Delay-Sensitive Applications [Link] [Slides]

Abstract:
Emerging multi-hop wireless networks provide a low-cost and flexible infrastructure that can be simultaneously utilized by multiple users for a variety of applications, including delay-sensitive applications, such as multimedia streaming, mission-critical applications, etc. However, this wireless infrastructure is often unreliable and provides dynamically varying resources with only limited QoS support. To improve the performance of the delay-sensitive applications and to support timely reaction to the network dynamics, the multi-hop network needs to be composed of autonomic nodes (agents), which can adapt, make their own transmission decisions and negotiate their wireless resources based on their available local information. Current wireless networking research has focused on coping with the environment disturbances, such as variations (uncertainties) of the wireless channel (e.g. fading) or source (e.g. multimedia traffic) characteristics, while neglecting the coupling dynamics among nodes, due to the shared nature of the wireless spectrum. However, characterizing and learning the neighboring nodes' actions and the evolution of these actions over time is vital in order to construct an efficient and robust solution for delay-sensitive applications.

Hence, we propose and analyze various interactive learning schemes for these agents to learn the network dynamics and, based on this knowledge, foresightedly adapt their cross-layer transmission decisions such that they can efficiently utilize the shared, time-varying network resources. We show that the foresighted decision making significantly improves the agents' utilities under a variety of dynamic network scenarios (e.g. multimedia streaming over WLAN, energy-efficient transmission in mobile ad hoc networks, joint route/channel selection in multi-hop cognitive radio networks) and various network topologies as compared to existing state-of-the-art solutions. In conclusion, our research adds a new, "cognitive", dimension to existing multi-hop wireless networks that enables the autonomic nodes to dynamically forecast the expected response to network dynamics of neighboring nodes and evaluate how specific forms of explicit and implicit signaling impact the performance of delay-sensitive applications.

Yi Su (Graduation Date: May 2010),
Title: Informationally Efficient Multi-User Communication [Link] [Slides]

Abstract:
The rapid increase in the demand for data rate over wired and wireless communication networks has led to a rethinking of the traditional network architecture and design principles. In fact, communication systems are inherently informationally decentralized competitive environments, where multiple devices executing a variety of applications and services need to locally adapt their transmission strategies based on their available information and compete for scarce networking resources. The concepts and techniques that have dominated multi-user communication research in recent years are not well suited for these informationally decentralized environments. Specifically, most existing research has focused on two extreme multi-user interaction scenarios, the complete information scenario with a common system-wide objective (e.g. Pareto optimality) and the private information scenario with conflicting objectives (e.g. Nash equilibrium (NE)).

The objective of this dissertation is to characterize users' optimal strategies to improve their performance subject to varying degrees of informational constraints. We mainly focus on fully distributed solutions without any real-time information exchange between different users. In particular, we investigate three key problems in information-constrained multi-user communication systems. First, when will a distributed algorithm (e.g. best response dynamics) converge to a NE? And how fast? Second, if information is constrained and no real-time information exchange between users is allowed, how to improve an inefficient NE without message passing? Last, assuming no real-time information exchange between users, can we still achieve Pareto optimality? We propose and analyze two new classes of games named additively coupled sum constrained games and linearly coupled games, in which we individually address these three questions. In particular, we provide sufficient conditions under which a unique NE exists and best response dynamics linearly converges to the NE. We also provide conjectural equilibrium based solutions that can substantially improve the performance of inefficient NE and fully recover Pareto optimality without any real-time information exchange between users. The proposed game models apply to a variety of realistic applications in multi-user communication systems, including multi-channel power control, flow control, and wireless random access.

Fangwen Fu (Graduation Date: May 2010),
Title: A Unified Framework for Delay-Sensitive Communications [Link] [Slides]

Abstract:
Delay-sensitive communications (e.g. multimedia transmission) are booming over a variety of wireless networks. Current solutions often lead to an unsatisfactory experience for delay-sensitive applications since they ignore the stringent delay requirements and the heterogeneous features (e.g. importance, delay deadlines and dependencies) of the delaysensitive data. This problem is becoming increasingly more serious when multiple delaysensitive applications coexist in wireless networks and share the scarce network resources. In this dissertation, we develop a unified foresighted optimization framework which explicitly considers both the heterogeneity of the delay-sensitive data and the dynamics of the wireless networks in order to optimize the long-term utilities of the delay-sensitive applications.

In the proposed unified framework, we establish three separation principles which are theoretically important for designing delay-sensitive communication systems. First, by introducing the post-decision states, we separate the foresighted decisions from the underlying network dynamics, which enables us to explore the structures of the optimal solutions and design low-complexity algorithms. Second, in order to explicitly consider the heterogeneity of the multimedia traffic, we prioritize the delay-sensitive data (expressed as direct acyclic graphs) and separate the multi-data unit foresighted decision into multiple single-data unit foresighted decisions, which can subsequently be performed from the high priority to the low priority. Third, when multiple delay-sensitive applications coexist in the wireless network, by introducing a resource price and relaxing the network resource constraints imposed in the future transmission, we separate the multi-user foresighted decision into multiple single-user foresighted decision, thereby significantly reducing the computation and communication complexity. Implementing the above framework in practice requires statistical knowledge of the network dynamics, which is often unavailable before transmission time. To overcome this obstacle, we propose novel structure-aware online learning algorithms derived from the above three separation principles. The proposed online learning algorithms have low complexity and fast convergence, and achieve ?-optimal solutions, which can significantly improve the delay-sensitive communication performance in the unknown environments.

Nicholas Mastronarde (Graduation Date: June 2011),
Title: Online Learning for Energy-Efficient Multimedia Systems [Link][Slides]

Abstract:
Designing energy-efficient resource management strategies for multimedia applications is a challenging problem because of their stringent delay constraints, mixed priorities, intense resource requirements, and sophisticated source coding dependency structures. In my dissertation research, I aim to develop a rigorous, formal, and unified modeling framework for supporting such applications in a large class of resource management scenarios including energy-efficient point-to-point wireless communication, cooperative multi-user wireless video transmission, and energy-efficient cross-layer system optimization.

The foundation of my approach is modeling multimedia systems as stochastic and dynamic systems. Unlike traditional resource management solutions, in which the goal is to optimize the immediate utility (i.e. myopic optimization), the goal in the proposed framework is to optimize the trajectory of the system's underlying stochastic process by accounting for how decisions at the current time impact the future utility (i.e. dynamic optimization). To achieve this, I model system and network resource management problems as Markov decision processes (MDPs). Then, to address the fact that the statistics of the system's underlying stochastic process are typically unknown a priori, I adopt and often extend reinforcement learning techniques from artificial intelligence to enable devices to learn their experienced dynamics online, at run-time, in order to optimize their long-term performance.

Shaolei Ren (Graduation Date: June 2012),
Title: Strategic Pricing and Resource Allocation: Framework and Applications [Link][Slides]

Abstract:
Enabled by ubiquitous broadband connectivity and seamless wireless connections, we have witnessed in the past few years the emergence of a plethora of wireless applications, ranging from data communications and social networking to the more recently wireless cloud computing. The growing tension between the exploding demand for such wireless applications and the increasingly scarce network resources (e.g., spectrum, power) has urged a rethinking of the service providers' pricing strategies and network resource management techniques to cope with potential threats of quality-of-service degradation and revenue decreases. Specifically, it has become of paramount importance for service providers to strategically redesign their pricing policies and to understand how various pricing policies will affect the service demand, competition in the market, as well as the network resource management.

In this dissertation, I propose a novel framework to optimize a service provider's pricing policy as well as its network resource allocation decision for profit maximization, in the presence of self-interested participating users that strategically respond to the charged price to maximize their own benefits. Applicable to both static and stochastic environments, the proposed framework explicitly takes into account user heterogeneity, which is observed in a wide range of applications. Based on the framework, I investigate the problem of optimizing pricing and resource allocation for the service provider's profit maximization in various contexts, including cooperative relay networks, communications markets, online user-generated content platforms, and mobile cloud computing systems.


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