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Publications of Shaul Markovitch

Show only papers coauthored with: Eugene Agichtein, Asaf Amit, David Carmel, Ido Dagan, Dmitry Davidov, Sagie Davidovich, Carmel Domshlak, Ofer Egozi, Saher Esmeir, Lev Finkelstein, Evgeniy Gabrilovich, Assaf Glazer, Orna Grumberg, Nela Gurevich, Yaniv Hamo, Reuven Hasson, Erez Karpas, Michael Katz, Uri Keidar, Oleg Ledeniov, Omer Levy, Sonya Liberman, Jeffrey Liker, Michael Lindenbaum, Shlomi Livne, David Lorenz, Shaul Losada, Marcial Losada, Shaul Marcus, Gretchen Marie., Poppy McLeod, Kira Radinsky, Ariel Raviv, Ronit Reger, Ehud Rivlin, Irit Rosdeutscher, Danny Rosenstein, Dmitry Rusakov, Paul Scott, Yaron Sella, Asaf Shatil, Oren Shnitzer, Sharon Spreitzer, Erez Webman
  1. Assaf Glazer, Michael Lindenbaum and Shaul Markovitch. Learning High-Density Regions for a Generalized Kolmogorov-Smirnov Test in High-Dimensional Data. In Proceedings of The 26th Conference on Neural Information Processing Systems (NIPS-2012), Lake Tahoe, Nevada, 2012.[abstract][pdf]
  2. Assaf Glazer, Michael Lindenbaum and Shaul Markovitch. One-Class Background Model. In The 11th Asian Conference on Computer Vision (ACCV-2012), Daejeon, Korea, 2012.[abstract]
  3. Assaf Glazer, Michael Lindenbaum and Shaul Markovitch. Feature Shift Detection. In 21st International Conference on Pattern Recognition (ICPR-2012), Tsukuba, Japan, 2012.[abstract]
  4. Carmel Domshlak, Erez Karpas and Shaul Markovitch. Online Speedup Learning for Optimal Planning. Journal of Artificial Intelligence Research, 44:709-755 2012.[abstract][pdf]
  5. Omer Levy and Shaul Markovitch. Teaching Machines to Learn by Metaphors. In Proceedings of the Twenty-Sixth AAAI Conference on Artificial Intelligence, 991-997 Toronto, Canada, 2012.[abstract][pdf]
  6. Ariel Raviv and Shaul Markovitch. Concept-Based Approach to Word-Sense Disambiguation. In Proceedings of the Twenty-Sixth AAAI Conference on Artificial Intelligence, 807-813 Toronto, Canada, 2012.[abstract][pdf]
  7. Kira Radinsky, Sagie Davidovich and Shaul Markovitch. Learning Causality for News Events Prediction. In Proceedings of WWW 2012, 909-918 Lyon, France, 2011.[abstract][pdf]
  8. Kira Radinsky, Sagie Davidovich and Shaul Markovitch. Learning Causality from Textual Data. In Proceedings of the IJCAI Workshop on Learning by Reading and its Applications in Intelligent Question-Answering, 363-367 Barcelona, Spain, 2011.[abstract][pdf]
  9. Erez Karpas, Michael Katz and Shaul Markovitch. When Optimal is Just Not Good Enough: Fast Near-Optimal Action Cost-Partitioning. In Proceedings of the 21st International Conference on Automated Planning and Scheduling, 122-129 Freiburg, Germany, 2011.[abstract][pdf]
  10. Saher Esmeir and Shaul Markovitch. Anytime Learning of Anycost Classifiers. Machine Learning, 82:445-473 2011.[abstract][pdf]
  11. Ofer Egozi, Shaul Markovitch and Evgeniy Gabrilovich. Concept-Based Information Retrieval using Explicit Semantic Analysis. {ACM} {T}ransactions on {I}nformation {S}ystems, 29:8:1-8:34 2011.[abstract][pdf]
  12. Kira Radinsky, Eugene Agichtein, Evgeniy Gabrilovich and Shaul Markovitch. A Word at a Time: Computing Word Relatedness using Temporal Semantic Analysis. In Proceedings of the 20th International World Wide Web Conference, 337-346 Hyderabad, India, 2011.[abstract][pdf]
  13. Carmel Domshlak, Erez Karpas and Shaul Markovitch. To Max or Not to Max: Online Learning for Speeding Up Optimal Planning. In Proceedings of the Twenty-Fourth AAAI Conference on Artificial Intelligence, 1071-1076 Atlanta, Georgia, 2010.[abstract][pdf]
  14. Carmel Domshlak, Erez Karpas and Shaul Markovitch. Learning to Combine Admissible Heuristics Under Bounded Time. In Proceedings of the ICAPS 2009 Workshop on Planning and Learning, Thessaloniki, Greece, 2009.[abstract][pdf]
  15. Sonya Liberman and Shaul Markovitch. Compact Hierarchical Explicit Semantic Representation. In Proceedings of the IJCAI 2009 Workshop on User-Contributed Knowledge and Artificial Intelligence: An Evolving Synergy (WikiAI09), Pasadena, CA, 2009.[abstract][pdf]
  16. Evgeniy Gabrilovich and Shaul Markovitch. Wikipedia-based Semantic Interpretation for Natural Language Processing. Journal of Artificial Intelligence Research, 34:443-498 2009.[abstract][pdf]
  17. Kira Radinsky, Sagie Davidovich and Shaul Markovitch. Predicting the News of Tomorrow Using Patterns inWeb Search Queries. In Proceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence (WI'08), 363-367 Sydney, Australia, 2008.[abstract][pdf]
  18. Saher Esmeir and Shaul Markovitch. Anytime Induction of Low-cost, Low-error Classifiers: a Sampling-based Approach. Journal of Artificial Intelligence Research, 33:1-31 2008.[abstract][pdf]
  19. Ofer Egozi, Evgeniy Gabrilovich and Shaul Markovitch. Concept-Based Feature Generation and Selection for Information Retrieval. In Proceedings of the Twenty-Third AAAI Conference on Artificial Intelligence, 1132-1137 Chicago, IL, 2008.[abstract][pdf]
  20. Saher Esmeir and Shaul Markovitch. Anytime Induction of Cost-sensitive Trees. In Proceedings of The 21st Conference on Neural Information Processing Systems (NIPS-2007), Vancouver, Canada, 2007.[abstract][pdf]
  21. Evgeniy Gabrilovich and Shaul Markovitch. Harnessing the Expertise of 70,000 Human Editors: Knowledge-Based Feature Generation for Text Categorization. Journal of Machine Learning Research, 8:2297-2345 2007.[abstract][pdf]
  22. Saher Esmeir and Shaul Markovitch. Anytime Learning of Decision Trees. Journal of Machine Learning Research, 8:891-933 2007.[abstract][pdf]
  23. Saher Esmeir and Shaul Markovitch. Occam's Razor Just Got Sharper. In Proceedings of The Twentieth International Joint Conference for Artificial Intelligence, 768-773 Hyderabad, India, 2007.[abstract][pdf]
  24. Evgeniy Gabrilovich and Shaul Markovitch. Computing Semantic Relatedness using Wikipedia-based Explicit Semantic Analysis. In Proceedings of The Twentieth International Joint Conference for Artificial Intelligence, 1606-1611 Hyderabad, India, 2007.[abstract][pdf]
  25. Saher Esmeir and Shaul Markovitch. When a Decision Tree Learner Has Plenty of Time. In Proceedings of the Twenty-First National Conference on Artificial Intelligence, 1597-1600 Boston, MA, 2006.[abstract][pdf]
  26. Saher Esmeir and Shaul Markovitch. Anytime Induction of Decision Trees: an Iterative Improvement Approach. In Proceedings of the Twenty-First National Conference on Artificial Intelligence, 348-355 Boston, MA, 2006.[abstract][pdf]
  27. Nela Gurevich, Shaul Markovitch and Ehud Rivlin. Active Learning with Near Misses. In Proceedings of the Twenty-First National Conference on Artificial Intelligence, 362-367 Boston, MA, 2006.[abstract][pdf]
  28. Evgeniy Gabrilovich and Shaul Markovitch. Overcoming the Brittleness Bottleneck using Wikipedia: Enhancing Text Categorization with Encyclopedic Knowledge. In Proceedings of the Twenty-First National Conference on Artificial Intelligence, 1301-1306 Boston, MA, 2006.[abstract][pdf]
  29. Dmitry Davidov and Shaul Markovitch. Multiple-goal Heuristic Search. Journal of Artificial Intelligence Research, 26:417-451 2006.[abstract][pdf]
  30. Asaf Amit and Shaul Markovitch. Learning to Bid in Bridge. Machine Learning, 63:287-327 2006.[abstract][pdf]
  31. Shaul Markovitch and Oren Shnitzer. Self-consistent Batch-Classification. Technical report CIS-2005-04, Technion, 2005.[abstract][pdf]
  32. Saher Esmeir and Shaul Markovitch. Interruptible Anytime Algorithms for Iterative Improvement of Decision Trees. In Proceedings of the 1st international workshop on Utility-based data mining, 78-85 Chicago, Illinois, 2005.[abstract][pdf]
  33. Yaniv Hamo and Shaul Markovitch. The Compset Algorithm for Subset Selection. In Proceedings of The Nineteenth International Joint Conference for Artificial Intelligence, 728-733 Edinburgh, Scotland, 2005.[abstract][pdf]
  34. Evgeniy Gabrilovich and Shaul Markovitch. Feature Generation for Text Categorization Using World Knowledge. In Proceedings of The Nineteenth International Joint Conference for Artificial Intelligence, 1048-1053 Edinburgh, Scotland, 2005.[abstract][pdf]
  35. Shaul Markovitch and Ronit Reger. Learning and Exploiting Relative Weaknesses of Opponent Agents. Autonomous Agents and Multi-agent Systems, 10:103-130 2005.[abstract][pdf]
  36. Evgeniy Gabrilovich and Shaul Markovitch. Text Categorization with Many Redundant Features: Using Aggressive Feature Selection to Make SVMs Competitive with C4.5. In Proceedings of The Twenty-First International Conference on Machine Learning, 321-328 Banff, Alberta, Canada, 2004.Morgan Kaufmann[abstract][pdf]
  37. Dmitry Davidov, Evgeniy Gabrilovich and Shaul Markovitch. Parameterized Generation of Labeled Datasets for Text Categorization Based on a Hierarchical Directory. In Proceedings of The 27th Annual International ACM SIGIR Conference, 250-257 Sheffield, UK, 2004.ACM Press[abstract][pdf]
  38. Saher Esmeir and Shaul Markovitch. Lookahead-based Algorithms for Anytime Induction of Decision Trees. In Proceedings of The Twenty-First International Conference on Machine Learning, 257-264 Banff, Alberta, Canada, 2004.Morgan Kaufmann[abstract][pdf]
  39. Michael Lindenbaum, Shaul Markovitch and Dmitry Rusakov. Selective Sampling for Nearest Neighbor Classifiers. Machine Learning, 54:125-152 2004.[abstract][pdf]
  40. Lev Finkelstein, Shaul Markovitch and Ehud Rivlin. Optimal Schedules for Parallelizing Anytime Algorithms: The Case of Shared Resources. Journal of Artificial Intelligence Research, 19:73-138 2003.[abstract][pdf]
  41. Shaul Markovitch and Asaf Shatil. Speedup Learning for Repair-based Search by Identifying Redundant Steps. Journal of Machine Learning Research, 4:649-682 2003.[abstract][pdf]
  42. Orna Grumberg, Shlomi Livne and Shaul Markovitch. Learning to Order {BDD} Variables in Verification. Journal of Artificial Intelligence Research, 18:83-116 2003.[abstract][pdf]
  43. Lev Finkelstein, Shaul Markovitch and Ehud Rivlin. Optimal Schedules for Parallelizing Anytime Algorithms: The Case of Independent Processes. In Proceedings of the Eighteenth National Conference on Artificial Intelligence, 719-724 Edmonton, Alberta, Canada, 2002.[abstract][pdf]
  44. Dmitry Davidov and Shaul Markovitch. Multiple-goal Search Algorithms and their Application to Web Crawling. In Proceedings of the Eighteenth National Conference on Artificial Intelligence, 713-718 Edmonton, Alberta, Canada, 2002.[abstract][pdf]
  45. Shaul Markovitch and Danny Rosenstein. Feature Generation Using General Constructor Functions. Machine Learning, 49:59-98 2002.[abstract][pdf]
  46. Lev Finkelstein, Shaul Markovitch and Ehud Rivlin. Optimal Schedules for Parallelizing Anytime Algorithms. In Proceedings of The AAAI Fall Symposium on Using Uncertainty within Computation, 49-56 North Carolina, 2001.[abstract][pdf]
  47. Lev Finkelstein and Shaul Markovitch. Optimal schedules for monitoring anytime algorithms. Artificial Intelligence, 126:63-108 2001.[abstract][pdf]
  48. Shaul Markovitch. Applications of Macro Learning to Path Planning. Technical report CIS9907, Technion, 1999.[abstract][pdf]
  49. Michael Lindenbaum, Shaul Markovitch and Dmitry Rusakov. Selective Sampling for Nearest Neighbor Classifiers. In The Proceedings of the Sixteenth National Confernce on Artificial Intelligence, 366-371 Orlando, Florida, 1999.[abstract][pdf]
  50. David Carmel and Shaul Markovitch. Exploration Strategies for Model-based Learning in Multiagent Systems. Autonomous Agents and Multi-agent Systems, 2:141-172 1999.[abstract][pdf]
  51. Oleg Ledeniov and Shaul Markovitch. The Divide-and-Conquer Subgoal-Ordering Algorithm for Speeding up Logic Inference. Journal of Artificial Intelligence Research, 9:37-97 1998.[abstract][pdf]
  52. Lev Finkelstein and Shaul Markovitch. A Selective Macro-learning Algorithm and its Application to the NxN Sliding-Tile Puzzle. Journal of Artificial Intelligence Research, 8:223-263 1998.[abstract][pdf]
  53. David Carmel and Shaul Markovitch. Model-based Learning of Interaction Strategies in Multi-Agent Systems. Journal of Experimental and Theoretical Artificial Intelligence, 10:309-332 1998.[abstract][pdf]
  54. David Carmel and Shaul Markovitch. Pruning Algorithms for Multi-Model Adversary Search. Artificial Intelligence, 99:325-355 1998.[abstract][pdf]
  55. Lev Finkelstein and Shaul Markovitch. Learning to Play Chess Selectively by Acquiring Move Patterns. ICCA Journal, 21:100-119 1998.[abstract][pdf]
  56. Oleg Ledeniov and Shaul Markovitch. Learning Investment Functions for Controlling the Utility of Control Knowledge. In Proceedings of the Fifteenth National Conference on Artificial Intelligence, 463-468 Madison, Wisconsin, 1998.[abstract][pdf]
  57. David Carmel and Shaul Markovitch. How to explore your opponent's strategy (almost) optimally. In Proceedings of the Third International Conference on Multi-Agent Systems, 64-71 Paris, France, 1998.[abstract][pdf]
  58. Oleg Ledeniov and Shaul Markovitch. Controlled Utilization of Control Knowledge for Speeding up Logic Inference. Technical Report CIS9812, Technion, 1998.[abstract][pdf]
  59. David Carmel and Shaul Markovitch. Exploration and Adaptation in Multiagent Systems: A Model-Based Approach. In Proceedings of The Fifteenth International Joint Conference for Artificial Intelligence, 606-611 Nagoya, Japan, 1997.[abstract][pdf]
  60. David Carmel and Shaul Markovitch. Incorporating Opponent Models into Adversary Search. In Proceedings of the Thirteenth National Conference on Artificial Intelligence, 120-125 Portland, Oregon, 1996.[abstract][pdf]
  61. David Carmel and Shaul Markovitch. Learning Models of Intelligent Agents. In Proceedings of the Thirteenth National Conference on Artificial Intelligence, 62-67 Portland, Oregon, 1996.[abstract][pdf]
  62. David Carmel and Shaul Markovitch. Learning and Using Opponent Models in Adversary Search. Technical Report CIS9609, Technion, 1996.[abstract][pdf]
  63. David Carmel and Shaul Markovitch. Opponent Modeling in Multi-agent Systems. In Gerhard Weiss and Sandip Sen, Editors, Adaption And Learning In Multi-Agent Systems. Springer-Verlag, 1996.[abstract][pdf]
  64. Shaul Markovitch and Yaron Sella. Learning of Resource Allocation Strategies for Game Playing. Computational Intelligence, 12:88-105 1996.[abstract][pdf]
  65. Uri Keidar, Shaul Markovitch and Erez Webman. Utilization Filtering of Macros Based on Goal Similarity. Technical Report CIS9608, Technion, 1996.[abstract][pdf]
  66. Ido Dagan, Shaul Marcus and Shaul Markovitch. Contextual Word Similarity and Estimation from Sparse Data. Computer Speech and Language, 9:123-152 1995.[abstract][pdf]
  67. David Carmel and Shaul Markovitch. The M* Algorithm: Incorporating Opponent Models Into Adversary Search. Technical Report CIS9402, Computer Science Department, Technion, 1994.[abstract][pdf]
  68. Shaul Markovitch and Paul Scott. Information Filtering: Selection Mechanisms in Learning Systems. Machine Learning, 10:113-151 1993.[abstract][pdf]
  69. Shaul Markovitch and Yaron Sella. Learning of Resource Allocation Strategies for game Playing. In Proceedings of The Thirteenth International Joint Conference for Artificial Intelligence, 974-979 Chambery, France, 1993.[abstract][pdf]
  70. David Carmel and Shaul Markovitch. Learning Models of the Opponent's Strategy in Game Playing. In Proceedings of The AAAI Fall Symposium on Games: Planing and Learning, 140-147 North Carolina, 1993.[abstract][pdf]
  71. David Lorenz and Shaul Markovitch. Derivative Evaluation Function Learning Using Genetic Operators. In Proceedings of The AAAI Fall Symposium on Games: Planing and Learning, 106-114 New Carolina, 1993.[abstract][pdf]
  72. Ido Dagan, Shaul Marcus and Shaul Markovitch. Contextual Word Similarity and Estimation from Sparse Data. In Proceedings of the 31st Annual Meeting of the Association for Computational Linguistics, 164-171 Ohio State University, 1993.[abstract][pdf]
  73. Paul Scott and Shaul Markovitch. Experience Selection and Problem Choice in an Exploratory Learning System. Machine Learning, 12:49-67 1993.[abstract][pdf]
  74. Shaul Markovitch and Irit Rosdeutscher. Systematic Experimentation with Deductive Learning: Satisficing vs. Optimizing Search. In Proceedings of the Knowledge Compilation and Speedup Learning Workshop, Aberdeen, Scotland, 1992.[abstract][pdf]
  75. Paul Scott and Shaul Markovitch. Representation Generation in An Exploratory Learning System. In D. Fisher and M. Pazzani, Editors, Concept Formation: Knowledge and Experience in Unsupervised Learning. Morgan Kaufmann, 1991.[abstract][pdf]
  76. Reuven Hasson, Shaul Markovitch and Yaron Sella. Using Filters to Improve Efficiency of Game-playing Learning Procedures. In Proceedings of Eleventh International Conference of the Chilean Computer Science Society, 125-137 Santiago, Chile, 1991.[abstract][pdf]
  77. Paul Scott and Shaul Markovitch. Knowledge Considered Harmful. In Proceedings of IEEE Colloquium on Knowledge Engineering, London, 1990.[abstract][pdf]
  78. Marcial Losada and Shaul Markovitch. GroupAnalyzer: {A} System for Dynamic Analysis of Group Interaction. In Proceedings of 23rd Hawaii International Conference for System Sciences, 101-110 Kailua-Kona,Hawaii, 1990.[abstract][pdf]
  79. Shaul Markovitch and Paul Scott. Utilization Filtering: a Method for Reducing the Inherent Harmfulness of Deductively Learned Knowledge. In Proceedings of The Eleventh International Joint Conference for Artificial Intelligence, 738-743 Detroit, Michigan, 1989.[abstract][pdf]
  80. Shaul Markovitch and Paul Scott. Information Filters and Their Implementation in the {SYLLOG} System. In Proceedings of The Sixth International Workshop on Machine Learning, 404-407 Ithaca, New York, 1989.Morgan Kaufmann[abstract][pdf]
  81. Shaul Markovitch. Information Filtering: Selection Mechanisms in Learning Systems. PhD Thesis, EECS Department, University of Michigan, 1989.[abstract][pdf]
  82. Shaul Markovitch and Paul Scott. Automatic Ordering of Subgoals --- A Machine Learning Approach. In Proceedings of the North American Conference on Logic Programming, 224-242 Cleveland, Ohio, USA, 1989.[abstract][pdf]
  83. Paul Scott and Shaul Markovitch. Uncertainty Based Selection of Learning Experiences. In Proceedings of The Sixth International Workshop on Machine Learning, 358-361 Ithaca, New York, 1989.Morgan Kaufmann[abstract][pdf]
  84. Paul Scott and Shaul Markovitch. Learning Novel Domains Through Curiosity and Conjecture. In Proceedings of International Joint Conference for Artificial Intelligence, 669-674 Detroit, Michigan, 1989.[abstract][pdf]
  85. Poppy McLeod, Jeffrey Liker, Sharon Spreitzer, Gretchen Marie. and Shaul Losada. Process feedback in task-oriented small groups. Working Paper 602, University of Michigan. School of Business Administration. Division of Research, 1989.[abstract][pdf]
  86. Shaul Markovitch and Paul Scott. The Role of Forgetting in Learning. In Proceedings of The Fifth International Conference on Machine Learning, 459-465 Ann Arbor, MI, 1988.Morgan Kaufmann[abstract][pdf]
  87. Shaul Markovitch and Paul Scott. Knowledge Considered Harmful. Technical Report 030788, The Center for Machine Intelligence, Ann Arbor, MI, 1988.[abstract][pdf]