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Publications of Shaul Markovitch

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  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]