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Note: Links lead to the DBLP on the Web. Lyle H. Ungar 17 Andrew I. Schein , Alexandrin Popescul , Lyle H. Ungar, David M. Pennock : Methods and metrics for cold-start recommendations. SIGIR 2002 : 253-260 16 Panos M. Markopoulos , Lyle H. Ungar: Pricing price information in e-commerce. ACM Conference on Electronic Commerce 2001 : 260-263 15 David C. Parkes , Lyle H. Ungar: An auction-based method for decentralized train scheduling. Agents 2001 : 43-50 14 Eugen C. Buehler , Lyle H. Ungar: Maximum entropy methods for biological sequence modeling. BIOKDD 2001 : 60-64 13 Gregory Z. Grudic , Lyle H. Ungar: Exploiting Multiple Secondary Reinforcers in Policy Gradient Reinforcement Learning. IJCAI 2001 : 965-972 12 Gregory Z. Grudic , Lyle H. Ungar: Rates of Convergence of Performance Gradient Estimates Using Function Approximation and Bias in Reinforcement Learning. NIPS 2001 : 1515-1522 11 Alexandrin Popescul , Lyle H. Ungar, David M. Pennock , Steve Lawrence : Probabilistic Models for Unified Collaborative and Content-Based Recommendation in Sparse-Data Environments. UAI 2001 : 437-444 10 Gregory Z. Grudic , Lyle H. Ungar: Localizing Search in Reinforcement Learning. AAAI/IAAI 2000 : 590-595 9 David C. Parkes , Lyle H. Ungar: Iterative Combinatorial Auctions: Theory and Practice. AAAI/IAAI 2000 : 74-81 8 David C. Parkes , Lyle H. Ungar: Preventing Strategic Manipulation in Iterative Auctions: Proxy Agents and Price-Adjustment. AAAI/IAAI 2000 : 82-89 7 Alexandrin Popescul , Gary William Flake , Steve Lawrence , Lyle H. Ungar, C. Lee Giles : Clustering and Identifying Temporal Trends in Document Databases. ADL 2000 : 173-182 6 Gregory Z. Grudic , Lyle H. Ungar: Localizing Policy Gradient Estimates to Action Transition. ICML 2000 : 343-350 5 Andrew McCallum , Kamal Nigam , Lyle H. Ungar: Efficient clustering of high-dimensional data sets with application to reference matching. KDD 2000 : 169-178 4 David C. Parkes , Lyle H. Ungar, Dean P. Foster : Accounting for Cognitive Costs in On-Line Auction Design. AMET 1998 : 25-40 3 Dale Schuurmans , Lyle H. Ungar, Dean P. Foster : Characterizing the generalization performance of model selection strategies. ICML 1997 : 340-348 2 Marcos Salganicoff , Lyle H. Ungar, Ruzena Bajcsy : Active Learning for Vision-Based Robot Grasping. Machine Learning 23 (2-3): 251-278 (1996) 1 Marcos Salganicoff , Lyle H. Ungar: Active Exploration and Learning in real-Valued Spaces using Multi-Armed Bandit Allocation Indices. ICML 1995 : 480-487  DiSC'03 © 2003 Association for Computing Machinery |
