Associate Professor Shaul Markovitch

Associate Professor Shaul Markovitch

Contact information
Homepage:
http://www.cs.technion.ac.il/~shaulm/
Email:
shaulm[at]cs.technion.ac.il
Office:
609
Phone:
4346
Office Hours:
Sunday, 12:30-13:30 by appointment room 37 מזכירות לימודי הסמכה
Research interests
Artificial Intelligence; Machine Learning; Multi-agent systems; Game playing; Opponent modeling; Search; Speedup Learning.
Selected publications
  • Evgeniy Gabrilovich and Shaul Markovitch.
    Wikipedia-based Semantic Interpretation.
    Accepted for Publication in Journal of Artificial Intelligence Research, ():, 2009 [bibtex]
  • 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), , 2008 [bibtex]
  • 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 [bibtex]
  • 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, , 2008 [bibtex]
  • Saher Esmeir and Shaul Markovitch.
    Anytime Induction of Cost-sensitive Trees.
    In Proceedings of The 21st Conference on Neural Information Processing Systems (NIPS-2007), , 2007 [bibtex]
  • 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 [bibtex]
  • Saher Esmeir and Shaul Markovitch.
    Anytime Learning of Decision Trees.
    Journal of Machine Learning Research, 8():891--933, 2007 [bibtex]
  • Saher Esmeir and Shaul Markovitch.
    Occam's Razor Just Got Sharper.
    In Proceedings of The Twentieth International Joint Conference for Artificial Intelligence, 768--773, 2007 [bibtex]
  • 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, 2007 [bibtex]
  • 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, 2006 [bibtex]
  • 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, 2006 [bibtex]
  • 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, 2006 [bibtex]
  • 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, 2006 [bibtex]
  • Dmitry Davidov and Shaul Markovitch.
    Multiple-goal Heuristic Search.
    Journal of Artificial Intelligence Research, 26():417--451, 2006 [bibtex]
  • Asaf Amit and Shaul Markovitch.
    Learning to Bid in Bridge.
    Machine Learning, 63(3):287--327, 2006 [bibtex]
  • Shaul Markovitch and Oren Shnitzer.
    Self-consistent Batch-Classification.
    No. CIS-2005-04, Technion, 2005 [bibtex]
  • 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, 2005 [bibtex]
  • Yaniv Hamo and Shaul Markovitch.
    The Compset Algorithm for Subset Selection.
    In Proceedings of The Nineteenth International Joint Conference for Artificial Intelligence, 728--733, 2005 [bibtex]
  • 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, 2005 [bibtex]
  • Shaul Markovitch and Ronit Reger.
    Learning and Exploiting Relative Weaknesses of Opponent Agents.
    Autonomous Agents and Multi-agent Systems, 10(2):103--130, 2005 [bibtex]
  • 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, Morgan Kaufmann, 2004 [bibtex]
  • 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, ACM Press, 2004 [bibtex]
  • 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, Morgan Kaufmann, 2004 [bibtex]
  • 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 [bibtex]
  • Shaul Markovitch and Asaf Shatil.
    Speedup Learning for Repair-based Search by Identifying Redundant Steps.
    Journal of Machine Learning Research, 4:649--682, 2003 [bibtex]
  • Orna Grumberg, Shlomi Livne and Shaul Markovitch.
    Learning to Order {BDD} Variables in Verification.
    Journal of Artificial Intelligence Research, 18:83-116, 2003 [bibtex]
  • 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, 2002 [bibtex]
  • 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, 2002 [bibtex]
  • 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, 2001 [bibtex]
  • Shaul Markovitch.
    Applications of Macro Learning to Path Planning.
    No. CIS9907, Technion, 1999 [bibtex]
  • Michael Lindenbaum, Shaul Markovitch and Dmitry Rusakov.
    Selective Sampling for Nearest Neighbor Classifiers.
    Machine Learning, 54(2):125--152, 2004 [bibtex]
  • 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, 1999 [bibtex]
  • Lev Finkelstein and Shaul Markovitch.
    Optimal schedules for monitoring anytime algorithms.
    Artificial Intelligence, 126:63-108, 2001 [bibtex]
  • Shaul Markovitch and Danny Rosenstein.
    Feature Generation Using General Constructor Functions.
    Machine Learning, 49:59--98, 2002 [bibtex]
  • David Carmel and Shaul Markovitch.
    Exploration Strategies for Model-based Learning in Multiagent Systems.
    Autonomous Agents and Multi-agent Systems, 2(2):141--172, 1999 [bibtex]
  • 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 [bibtex]
  • 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 [bibtex]
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