| 2009 | ||
|---|---|---|
| 49 | Paramveer S. Dhillon, Brian Tomasik, Dean P. Foster, Lyle H. Ungar: Multi-task Feature Selection Using the Multiple Inclusion Criterion (MIC). ECML/PKDD (1) 2009: 276-289 | |
| 48 | Ted Sandler, Lyle H. Ungar, Koby Crammer: Resolving Identity Uncertainty with Learned Random Walks. ICDM 2009: 457-465 | |
| 47 | Luís Sarmento, Alexander Kehlenbeck, Eugénio C. Oliveira, Lyle H. Ungar: Efficient Clustering of Web-Derived Data Sets. MLDM 2009: 398-412 | |
| 46 | Luís Sarmento, Alexander Kehlenbeck, Eugénio C. Oliveira, Lyle H. Ungar: An Approach to Web-Scale Named-Entity Disambiguation. MLDM 2009: 689-703 | |
| 45 | Mahesh Yaragatti, Ted Sandler, Lyle H. Ungar: A predictive model for identifying mini-regulatory modules in the mouse genome. Bioinformatics 25(3): 353-357 (2009) | |
| 44 | Paramveer S. Dhillon, Dean P. Foster, Lyle H. Ungar: Transfer Learning Using Feature Selection CoRR abs/0905.4022: (2009) | |
| 2008 | ||
| 43 | Perry Evans, Ted Sandler, Lyle H. Ungar: Protein-Protein Interaction Network Alignment by Quantitative Simulation. BIBM 2008: 325-328 | |
| 42 | Vasileios Kandylas, Lyle H. Ungar, Ted Sandler, Shane Jensen: Multiway Clustering for Creating Biomedical Term Sets. BIBM 2008: 449-452 | |
| 41 | Casey Whitelaw, Alexander Kehlenbeck, Nemanja Petrovic, Lyle H. Ungar: Web-scale named entity recognition. CIKM 2008: 123-132 | |
| 40 | Binyamin Rosenfeld, Ronen Feldman, Lyle H. Ungar: Using sequence classification for filtering web pages. CIKM 2008: 1355-1356 | |
| 39 | Paramveer S. Dhillon, Dean P. Foster, Lyle H. Ungar: Efficient Feature Selection in the Presence of Multiple Feature Classes. ICDM 2008: 779-784 | |
| 38 | Ted Sandler, John Blitzer, Partha Pratim Talukdar, Lyle H. Ungar: Regularized Learning with Networks of Features. NIPS 2008: 1401-1408 | |
| 37 | Ravi Aron, Lyle H. Ungar, Annapurna Valluri: A model of market power and efficiency in private electronic exchanges. European Journal of Operational Research 187(3): 922-942 (2008) | |
| 36 | Vasileios Kandylas, S. Phineas Upham, Lyle H. Ungar: Finding cohesive clusters for analyzing knowledge communities. Knowl. Inf. Syst. 17(3): 335-354 (2008) | |
| 2007 | ||
| 35 | Vasileios Kandylas, S. Phineas Upham, Lyle H. Ungar: Finding Cohesive Clusters for Analyzing Knowledge Communities. ICDM 2007: 203-212 | |
| 34 | Ronen Feldman, Moshe Fresko, Jacob Goldenberg, Oded Netzer, Lyle H. Ungar: Extracting Product Comparisons from Discussion Boards. ICDM 2007: 469-474 | |
| 33 | Andrew I. Schein, Lyle H. Ungar: Active learning for logistic regression: an evaluation. Machine Learning 68(3): 235-265 (2007) | |
| 2006 | ||
| 32 | Tina Eliassi-Rad, Lyle H. Ungar, Mark Craven, Dimitrios Gunopulos: Proceedings of the Twelfth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Philadelphia, PA, USA, August 20-23, 2006 ACM 2006 | |
| 31 | Jinying Chen, Andrew I. Schein, Lyle H. Ungar, Martha Palmer: An Empirical Study of the Behavior of Active Learning for Word Sense Disambiguation. HLT-NAACL 2006 | |
| 30 | Ted Sandler, Andrew I. Schein, Lyle H. Ungar: Automatic term list generation for entity tagging. Bioinformatics 22(6): 651-657 (2006) | |
| 29 | Jing Zhou, Dean P. Foster, Robert A. Stine, Lyle H. Ungar: Streamwise Feature Selection. Journal of Machine Learning Research 7: 1861-1885 (2006) | |
| 2005 | ||
| 28 | Panos M. Markopoulos, Ravi Aron, Lyle H. Ungar: Is Online Product Information Availability Driven by Quality or Differentiation? ICIS 2005 | |
| 27 | Jing Zhou, Dean P. Foster, Robert A. Stine, Lyle H. Ungar: Streaming feature selection using alpha-investing. KDD 2005: 384-393 | |
| 26 | Andrew I. Schein, Alexandrin Popescul, Lyle H. Ungar, David M. Pennock: CROC: A New Evaluation Criterion for Recommender Systems. Electronic Commerce Research 5(1): 51-74 (2005) | |
| 2004 | ||
| 25 | Alexandrin Popescul, Lyle H. Ungar: Cluster-based concept invention for statistical relational learning. KDD 2004: 665-670 | |
| 24 | Eugen C. Buehler, Jeffrey R. Sachs, Kui Shao, Ansuman Bagchi, Lyle H. Ungar: The CRASSS plug-in for integrating annotation data with hierarchical clustering results. Bioinformatics 20(17): 3266-3269 (2004) | |
| 23 | Phillip P. Le, Amit Bahl, Lyle H. Ungar: Using prior knowledge to improve genetic network reconstruction from microarray data. In Silico Biology 4: (2004) | |
| 2003 | ||
| 22 | Alexandrin Popescul, Lyle H. Ungar, Steve Lawrence, David M. Pennock: Statistical Relational Learning for Document Mining. ICDM 2003: 275-282 | |
| 21 | Panos M. Markopoulos, Ravi Aron, Lyle H. Ungar: Dual Pricing in Electronic Markets. ICIS 2003: 485-496 | |
| 20 | Dmitry Pavlov, Alexandrin Popescul, David M. Pennock, Lyle H. Ungar: Mixtures of Conditional Maximum Entropy Models. ICML 2003: 584-591 | |
| 19 | Seung-Taek Park, Alexy Khrabrov, David M. Pennock, Steve Lawrence, C. Lee Giles, Lyle H. Ungar: Static and Dynamic Analysis of the Internet's Susceptibility to Faults and Attacks. INFOCOM 2003 | |
| 2002 | ||
| 18 | Andrew I. Schein, Alexandrin Popescul, Lyle H. Ungar, David M. Pennock: Methods and metrics for cold-start recommendations. SIGIR 2002: 253-260 | |
| 2001 | ||
| 17 | Panos M. Markopoulos, Lyle H. Ungar: Pricing price information in e-commerce. ACM Conference on Electronic Commerce 2001: 260-263 | |
| 16 | David C. Parkes, Lyle H. Ungar: An auction-based method for decentralized train scheduling. Agents 2001: 43-50 | |
| 15 | Eugen C. Buehler, Lyle H. Ungar: Maximum entropy methods for biological sequence modeling. BIOKDD 2001: 60-64 | |
| 14 | Gregory Z. Grudic, Lyle H. Ungar: Exploiting Multiple Secondary Reinforcers in Policy Gradient Reinforcement Learning. IJCAI 2001: 965-972 | |
| 13 | 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 | |
| 12 | 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 | |
| 2000 | ||
| 11 | Gregory Z. Grudic, Lyle H. Ungar: Localizing Search in Reinforcement Learning. AAAI/IAAI 2000: 590-595 | |
| 10 | David C. Parkes, Lyle H. Ungar: Iterative Combinatorial Auctions: Theory and Practice. AAAI/IAAI 2000: 74-81 | |
| 9 | David C. Parkes, Lyle H. Ungar: Preventing Strategic Manipulation in Iterative Auctions: Proxy Agents and Price-Adjustment. AAAI/IAAI 2000: 82-89 | |
| 8 | 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 | |
| 7 | Gregory Z. Grudic, Lyle H. Ungar: Localizing Policy Gradient Estimates to Action Transition. ICML 2000: 343-350 | |
| 6 | Andrew McCallum, Kamal Nigam, Lyle H. Ungar: Efficient clustering of high-dimensional data sets with application to reference matching. KDD 2000: 169-178 | |
| 1998 | ||
| 5 | David C. Parkes, Lyle H. Ungar, Dean P. Foster: Accounting for Cognitive Costs in On-Line Auction Design. AMET 1998: 25-40 | |
| 1997 | ||
| 4 | Dale Schuurmans, Lyle H. Ungar, Dean P. Foster: Characterizing the generalization performance of model selection strategies. ICML 1997: 340-348 | |
| 1996 | ||
| 3 | Marcos Salganicoff, Lyle H. Ungar, Ruzena Bajcsy: Active Learning for Vision-Based Robot Grasping. Machine Learning 23(2-3): 251-278 (1996) | |
| 1995 | ||
| 2 | Marcos Salganicoff, Lyle H. Ungar: Active Exploration and Learning in real-Valued Spaces using Multi-Armed Bandit Allocation Indices. ICML 1995: 480-487 | |
| 1992 | ||
| 1 | Jonathan M. Vinson, Stephen D. Grantham, Lyle H. Ungar: Automatic Rebuilding of Qualitative Models for Diagnosis. IEEE Expert 7(4): 23-30 (1992) | |