Artificial Intelligence Professional Interest Community - Papers and Awards


Best Paper Awards


Selected Conference Papers

2009

  1. Elad Hazan, C. Seshadhri. Efficient learning algorithms for changing environments. The 26th International Conference on Machine Learning (ICML 2009)
  2. V. Sindhwani, P. Melville, R. Lawrence. Uncertainty Sampling and Transductive Experimental Design for Active Dual Supervision, ICML 2009
  3. R. Garg and R. Khandekar. Gradient Descent with Sparsification: An iterative algorithm for sparse recovery with restricted isometry property, ICML 2009
  4. D. Silver and G. Tesauro. Monte-Carlo Simulation Balancing, ICML 2009
  5. Y. Liu, A. Niculescu-Mizil, W. Gryc. Topic-Link LDA: Joint Models of Topic and Author Community, ICML 2009
  6. Alina Beygelzimer, Sanjoy Dasgupta, John Langford. Importance weighted active learning, ICML 2009
  7. Tessa Lau, Clemens Drews, and Jeffrey Nichols. Interpreting Written How-To Instructions, IJCAI 2009
  8. Tuan A. Nguyen, Minh Binh Do, Subbarao Kambhampati, Biplav Srivastava. Planning with Partial Preference Models, IJCAI 2009
  9. James Lin, Jeffrey Wong, Allen Cypher, Jeffrey Nichols, Tessa Lau. End-User Programming of Mashups with Vegemite, ACM International Conference on Intelligent User Interfaces (IUI 2009)
  10. Jeffrey P. Bigham, Tessa Lau, Jeffrey Nichols. TrailBlazer: Enabling Blind Users to Blaze Trails Through the Web, IUI 2009
  11. David Gotz and Zhen Wen. Behavior-Driven Visualization Recommendation, IUI 2009
  12. Wen-Huang Cheng and David Gotz. Context-Based Page Unit Recommendation for Web-Based Sensemaking Tasks, IUI 2009
  13. Elad Hazan and Nimrod Megiddo.� An Efficient Interior-Point Method for Minimum-Regret Learning in Online Convex Optimization, NIPS 2009.
  14. Guillermo A. Cecchi et al. Predictive Network Models of Schizophrenia. NIPS 2009
  15. Elad Hazan and S. Kale.� On Stochastic and Worst-case Models for Investing, NIPS 2009.
  16. Elad Hazan and S. Kale.� Online Submodular Minimization, NIPS 2009
  17. Aurelie Lozano, Grzegorz Swirszcz, Naoki Abe, Grouped Orthogonal Matching Pursuit for Variable Selection and Prediction, NIPS 2009
  18. Tetsuro Morimura, Eiji Uchibe, Junichiro Yoshimoto, Kenji Doya. A Generalized Natural Actor-Critic Algorithm, NIPS 2009.
  19. Vitaly Feldman. Robustness of Evolvability, Conference on Learning Theory (COLT), 2009.
  20. Janusz Marecki, Pradeep Varakantham, Jun-young Kwak, Matthew Taylor, Paul Scerri and Milind Tambe.� Exploiting Coordination Locales in Distributed POMDPs via Social Model Shaping, 19th International Conference on Automated Planning and Scheduling (ICAPS), 2009.
  21. Alina Beygelzimer, John Langford, Yuri Lifshits, Gregory Sorkin, Alexander Strehl: Conditional Probability Tree Estimation Analysis and Algorithms, UAI 2009.
  22. V. Feldman, V. Guruswami, P. Raghavendra and Yi Wu: Agnostic Learning of Monomials by Halfspaces is Hard, In IEEE Symposium on Foundations of Computer Science (FOCS), 2009.
  23. V. Feldman: A Complete Characterization of Statistical Query Learning with Applications to Evolvability. In IEEE Symposium on Foundations of Computer Science (FOCS), 2009.
  24. Elad Hazan and Satyen Kale. Better Algorithms for Benign Bandits. ACM-SIAM Symposium on Discrete Algorithms (SODA09)
  25. Alina Beygelzimer, John Langford: The offset tree for learning with partial labels. KDD 2009



Journals (2009):

  1. Experience-Induced Neural Circuits That Achieve High Capacity. V. Feldman and L. Valiant. Neural Computation 21:10 : pp. 2715-2754, 2009.
  2. On The Power of Membership Queries in Agnostic Learning. V. Feldman. Journal of Machine Learning Research 10 : pp. 163-182, 2009.
  3. Maria-Florina Balcan, Alina Beygelzimer, John Langford: Agnostic active learning. J. Comput. Syst. Sci. 75(1): 78-89 (2009)
  4. Tetsuro Morimura, Eiji Uchibe, Junichiro Yoshimoto, Jan Peters, Kenji Doya: Derivatives of Logarithmic Stationary Distributions for Policy Gradient Reinforcement Learning. Neural Computation.
  5. Vitaly Feldman: Hardness of approximate two-level logic minimization and PAC learning with membership queries. J. Comput. Syst. Sci. 75(1): 13-26 (2009)
  6. Vitaly Feldman, Shrenik Shah: Separating models of learning with faulty teachers. Theor. Comput. Sci. 410(19): 1903-1912 (2009)



Invited talks

2009

  1. Gerry Tesauro, at the Multidisiplinary Symposium on Reinforcement Learning (co-located with ICML/UAI/COLT).
  2. Ralph Linsker, NRI (Nanoelectronics Research Initiative) conference "Architectures for Post-CMOS Switches" (Notre Dame, Aug. 2009): "Brains, artificial neural networks, and some future hardware issues"
  3. Ralph Linsker, Talk to IBM Academy of Technology "Beyond Multicore" Conference (IBM, multiple sites, July 2009): "A multicore accelerator architecture for neural network and related applications," R. Linsker and M. Ritter
  4. Sam Adams: AGI Roadmap Workshop http://agi-roadmap.org/
  5. Vitaly Feldman: invited talk at STOC 2009 Valiant's birthday celebration, "On the Learning Power of Evolution"
  6. Vikas Sindhwani: Machine Learning Summer School, Chicago, June 2009; Institute of Pure and Applied Math (IPAM) at UCLA