DEPARTMENT OF COMPUTER SCIENCE AND ENGINEERING UNIVERSITY OF CALIFORNIA, SAN DIEGO CSE 254: Seminar on Learning Algorithms Spring 2002 List of Papers Each link below should be to a web page where the full text of the paper can be found. In many cases, other interesting papers are on these web pages also. Participants in the seminar should feel free to propose papers not on the list here, if these other papers are certainly good and worthwhile to present. The papers listed here are definitely interesting and worthwhile. An Alternate Objective Function for Markovian Fields. Sham Kakade, Yee Whye Teh and Sam T. Roweis, to appear, ICML 2002. (Contact the authors for a copy.) Maximum Entropy Markov Models for Information Extraction and Segmentation. Andrew Mccallum, Dayne Freitag, Fernando Pereira, ICML 2000. Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data. John Lafferty, Andrew McCallum, Fernando Pereira, ICML 2001. On Discriminative vs. Generative Classifiers: A comparison of logistic regression and Naive Bayes. Andrew Y. Ng and Michael Jordan. To appear in NIPS 14, 2002. Policy invariance under reward transformations: Theory and application to reward shaping, Andrew Y. Ng, Daishi Harada and Stuart Russell. In Proceedings of the Sixteenth International Conference on Machine Learning, 1999. On Feature Selection: Learning with Exponentially many Irrelevant Features as Training Examples, Andrew Y. Ng. In Proceedings of the Fifteenth International Conference on Machine Learning, 1998. Improving Text Classification by Shrinkage in a Hierarchy of Classes, Andrew McCallum, Roni Rosenfeld, Tom Mitchell and Andrew Y. Ng in Proceedings of the Fifteenth International Conference on Machine Learning, 1998. Learning Evaluation Functions for Global Optimization and Boolean Satisfiability, Boyan, J. A. and A. W. Moore, Fifteenth National Conference on Artificial Intelligence (AAAI), 1998. Balancing Multiple Sources of Reward in Reinforcement Learning, C.R. Shelton, Advances in Neural Information Processing Systems 2000 (NIPS 13), pp. 1082-1088. Using Eligibility Traces to Find the Best Memoryless Policy in Partially Observable Markov Decision Processes. J. Loch and S. Singh. In ICML 1998. Cobot: A Social Reinforcement Learning Agent. Charles Lee Isbell, Christian Shelton, Michael Kearns, Satinder Singh, Peter Stone, NIPS 2001. Reinforcement Learning for Spoken Dialogue Systems. S. Singh, M. Kearns, D. Litman and M. Walker. In NIPS 12, 1999 (Published 2000). Chapter 7 on the Luduan document retrieval system, in Finding Structure in Text, Genome, and Other Symbolic Sequences by Ted E. Dunning, Ph.D. thesis, University of Sheffield, 1998. Adaptive Probabilistic Networks with Hidden Variables by John Binder, Daphne Koller, Stuart Russell, Keiji Kanazawa. Machine Learning, 1997. Theoretical Views of Boosting and Applications, Rob Schapire, 1999. An adaptive version of the boost by majority algorithm. Y, Freund, Proceedings of the Twelfth Annual Conference on Computational Learning Theory, 1999. A Natural Law of Succession, Eric Sven Ristad, 1995. The Effect of Unlabeled Samples in Reducing the Small Sample Size Problem and Mitigating the Hughes Phenomenon, Behzad M. Shahshahani and David A. Landgrebe, IEEE Transactions on Geoscience and Remote Sensing, Vol. 32, No. 5, pp 1087-1095, September 1994. Text Classification from Labeled and Unlabeled Documents using EM. Kamal Nigam, Andrew McCallum, Sebastian Thrun and Tom Mitchell, Machine Learning, 39(2/3). pp. 103-134. 2000. A Comparison of Event Models for Naive Bayes Text Classification. Andrew McCallum and Kamal Nigam, AAAI/ICML-98 Workshop on Learning for Text Categorization, pp. 41-48. Technical Report WS-98-05. AAAI Press. 1998 Information Extraction with HMMs and Shrinkage. Dayne Freitag and Andrew McCallum. AAAI'99 Workshop on Machine Learning for Information Extraction. Fast Probabilistic Modeling for Combinatorial Optimization, Shumeet Baluja & Scott Davies, AAAI-98. Shrinking Trees (1990) Trevor Hastie, Daryl Pregibon Regression shrinkage and selection via the lasso R.J. Tibshirani, Technical report, University of Toronto, June 1994.