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ORCIDShaul Markovitch
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- affiliation: Technion - Israel Institute of Technology, Haifa, Israel
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2020 – today
- 2024
[c51]Daniel Gilo, Shaul Markovitch:
A General Search-Based Framework for Generating Textual Counterfactual Explanations. AAAI 2024: 18073-18081- 2023
- [c50]Adi Simhi, Shaul Markovitch:
Interpreting Embedding Spaces by Conceptualization. EMNLP 2023: 1704-1719 - 2022
- [i17]Adi Simhi, Shaul Markovitch:
Interpreting Embedding Spaces by Conceptualization. CoRR abs/2209.00445 (2022) - [i16]Daniel Gilo, Shaul Markovitch:
Anytime Generation of Counterfactual Explanations for Text Classification. CoRR abs/2211.00369 (2022) - 2020
- [c49]Guy Kushilevitz, Shaul Markovitch, Yoav Goldberg:
A Two-Stage Masked LM Method for Term Set Expansion. ACL 2020: 6829-6835 - [c48]Jonathan Zarecki, Shaul Markovitch:
Textual Membership Queries. IJCAI 2020: 2662-2668 - [i15]Guy Kushilevitz, Shaul Markovitch, Yoav Goldberg:
A Two-Stage Masked LM Method for Term Set Expansion. CoRR abs/2005.01063 (2020) - [i14]Michal Badian, Shaul Markovitch:
Knowledge-Based Learning through Feature Generation. CoRR abs/2006.03874 (2020)
2010 – 2019
- 2019
- [c47]Nurit Devir, Orna Grumberg, Shaul Markovitch, Gabi Nakibly:
Topology-Agnostic Runtime Detection of OSPF Routing Attacks. CNS 2019: 277-285 - 2018
- [i13]Lior Friedman, Shaul Markovitch:
Recursive Feature Generation for Knowledge-based Learning. CoRR abs/1802.00050 (2018) - [i12]Sarai Duek, Shaul Markovitch:
Automatic Generation of Language-Independent Features for Cross-Lingual Classification. CoRR abs/1802.04028 (2018) - [i11]Jonathan Zarecki, Shaul Markovitch:
Textual Membership Queries. CoRR abs/1805.04609 (2018) - 2017
- [c46]Yotam Eshel, Noam Cohen, Kira Radinsky, Shaul Markovitch, Ikuya Yamada, Omer Levy:
Named Entity Disambiguation for Noisy Text. CoNLL 2017: 58-68 - [e1]Satinder Singh, Shaul Markovitch:
Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, February 4-9, 2017, San Francisco, California, USA. AAAI Press 2017 [contents] - [i10]Yotam Eshel, Noam Cohen, Kira Radinsky, Shaul Markovitch, Ikuya Yamada, Omer Levy:
Named Entity Disambiguation for Noisy Text. CoRR abs/1706.09147 (2017) - 2015
- [c45]Omer Geiger, Shaul Markovitch:
Algorithmic Exam Generation. IJCAI 2015: 1149-1155 - 2014
- [c44]Assaf Glazer, Omer Weissbrod, Michael Lindenbaum, Shaul Markovitch:
Approximating Hierarchical MV-sets for Hierarchical Clustering. NIPS 2014: 999-1007 - [i9]Saher Esmeir, Shaul Markovitch:
Anytime Induction of Low-cost, Low-error Classifiers: a Sampling-based Approach. CoRR abs/1401.3447 (2014) - [i8]Evgeniy Gabrilovich, Shaul Markovitch:
Wikipedia-based Semantic Interpretation for Natural Language Processing. CoRR abs/1401.5697 (2014) - [i7]Carmel Domshlak, Erez Karpas, Shaul Markovitch:
Online Speedup Learning for Optimal Planning. CoRR abs/1401.5861 (2014) - [i6]Kira Radinsky, Sagie Davidovich, Shaul Markovitch:
Learning to Predict from Textual Data. CoRR abs/1402.0574 (2014) - 2013
- [c43]Assaf Glazer, Michael Lindenbaum, Shaul Markovitch:
q-OCSVM: A q-Quantile Estimator for High-Dimensional Distributions. NIPS 2013: 503-511 - 2012
- [j28]Carmel Domshlak, Erez Karpas, Shaul Markovitch:
Online Speedup Learning for Optimal Planning. J. Artif. Intell. Res. 44: 709-755 (2012) - [j27]Kira Radinsky, Sagie Davidovich, Shaul Markovitch:
Learning to Predict from Textual Data. J. Artif. Intell. Res. 45: 641-684 (2012) - [c42]Ariel Raviv, Shaul Markovitch:
Concept-Based Approach to Word-Sense Disambiguation. AAAI 2012: 807-813 - [c41]Omer Levy, Shaul Markovitch:
Teaching Machines to Learn by Metaphors. AAAI 2012: 991-997 - [c40]Assaf Glazer, Michael Lindenbaum, Shaul Markovitch:
One-Class Background Model. ACCV Workshops (1) 2012: 301-307 - [c39]Tamar Avraham, Ilya Gurvich, Michael Lindenbaum, Shaul Markovitch:
Learning Implicit Transfer for Person Re-identification. ECCV Workshops (1) 2012: 381-390 - [c38]Assaf Glazer, Michael Lindenbaum, Shaul Markovitch:
Feature shift detection. ICPR 2012: 1383-1386 - [c37]Assaf Glazer, Michael Lindenbaum, Shaul Markovitch:
Learning High-Density Regions for a Generalized Kolmogorov-Smirnov Test in High-Dimensional Data. NIPS 2012: 737-745 - [c36]Kira Radinsky, Sagie Davidovich, Shaul Markovitch:
Learning causality for news events prediction. WWW 2012: 909-918 - 2011
- [j26]Saher Esmeir, Shaul Markovitch:
Anytime learning of anycost classifiers. Mach. Learn. 82(3): 445-473 (2011) - [j25]Ofer Egozi, Shaul Markovitch, Evgeniy Gabrilovich
:
Concept-Based Information Retrieval Using Explicit Semantic Analysis. ACM Trans. Inf. Syst. 29(2): 8:1-8:34 (2011) - [c35]Erez Karpas, Michael Katz, Shaul Markovitch:
When Optimal Is Just Not Good Enough: Learning Fast Informative Action Cost Partitionings. ICAPS 2011 - [c34]Anat Hashavit, Shaul Markovitch:
Max-Prob: An Unbiased Rational Decision Making Procedure for Multiple-Adversary Environments. IJCAI 2011: 222-227 - [c33]Kira Radinsky, Eugene Agichtein, Evgeniy Gabrilovich, Shaul Markovitch:
A word at a time: computing word relatedness using temporal semantic analysis. WWW 2011: 337-346 - [i5]Oleg Ledeniov, Shaul Markovitch:
The Divide-and-Conquer Subgoal-Ordering Algorithm for Speeding up Logic Inference. CoRR abs/1105.5442 (2011) - [i4]Lev Finkelstein, Shaul Markovitch, Ehud Rivlin:
Optimal Schedules for Parallelizing Anytime Algorithms: The Case of Shared Resources. CoRR abs/1106.5269 (2011) - [i3]Orna Grumberg, Shlomi Livne, Shaul Markovitch:
Learning to Order BDD Variables in Verification. CoRR abs/1107.0020 (2011) - [i2]Dmitry Davidov, Shaul Markovitch:
Multiple-Goal Heuristic Search. CoRR abs/1109.6618 (2011) - 2010
- [c32]Carmel Domshlak, Erez Karpas, Shaul Markovitch:
To Max or Not to Max: Online Learning for Speeding Up Optimal Planning. AAAI 2010: 1071-1076
2000 – 2009
- 2009
- [j24]Evgeniy Gabrilovich, Shaul Markovitch:
Wikipedia-based Semantic Interpretation for Natural Language Processing. J. Artif. Intell. Res. 34: 443-498 (2009) - 2008
- [j23]Saher Esmeir, Shaul Markovitch:
Anytime Induction of Low-cost, Low-error Classifiers: a Sampling-based Approach. J. Artif. Intell. Res. 33: 1-31 (2008) - [c31]Ofer Egozi, Evgeniy Gabrilovich, Shaul Markovitch:
Concept-Based Feature Generation and Selection for Information Retrieval. AAAI 2008: 1132-1137 - [c30]Kira Radinsky, Sagie Davidovich, Shaul Markovitch:
Predicting theNews of Tomorrow Using Patterns in Web Search Queries. Web Intelligence 2008: 363-367 - 2007
- [j22]Saher Esmeir, Shaul Markovitch:
Anytime Learning of Decision Trees. J. Mach. Learn. Res. 8: 891-933 (2007) - [j21]Evgeniy Gabrilovich, Shaul Markovitch:
Harnessing the Expertise of 70, 000 Human Editors: Knowledge-Based Feature Generation for Text Categorization. J. Mach. Learn. Res. 8: 2297-2345 (2007) - [c29]Saher Esmeir, Shaul Markovitch:
Occam's Razor Just Got Sharper. IJCAI 2007: 768-773 - [c28]Evgeniy Gabrilovich, Shaul Markovitch:
Computing Semantic Relatedness Using Wikipedia-based Explicit Semantic Analysis. IJCAI 2007: 1606-1611 - [c27]Saher Esmeir, Shaul Markovitch:
Anytime Induction of Cost-sensitive Trees. NIPS 2007: 425-432 - 2006
- [j20]Dmitry Davidov, Shaul Markovitch:
Multiple-Goal Heuristic Search. J. Artif. Intell. Res. 26: 417-451 (2006) - [j19]Asaf Amit, Shaul Markovitch:
Learning to bid in bridge. Mach. Learn. 63(3): 287-327 (2006) - [c26]Saher Esmeir, Shaul Markovitch:
Anytime Induction of Decision Trees: An Iterative Improvement Approach. AAAI 2006: 348-355 - [c25]Nela Gurevich, Shaul Markovitch, Ehud Rivlin:
Active Learning with Near Misses. AAAI 2006: 362-367 - [c24]Evgeniy Gabrilovich, Shaul Markovitch:
Overcoming the Brittleness Bottleneck using Wikipedia: Enhancing Text Categorization with Encyclopedic Knowledge. AAAI 2006: 1301-1306 - [c23]Saher Esmeir, Shaul Markovitch:
When a Decision Tree Learner Has Plenty of Time. AAAI 2006: 1597-1600 - 2005
- [j18]Shaul Markovitch, Ronit Reger:
Learning and Exploiting Relative Weaknesses of Opponent Agents. Auton. Agents Multi Agent Syst. 10(2): 103-130 (2005) - [c22]Yaniv Hamo, Shaul Markovitch:
The COMPSET Algorithm for Subset Selection. IJCAI 2005: 728-733 - [c21]Evgeniy Gabrilovich, Shaul Markovitch:
Feature Generation for Text Categorization Using World Knowledge. IJCAI 2005: 1048-1053 - 2004
- [j17]Michael Lindenbaum, Shaul Markovitch, Dmitry Rusakov:
Selective Sampling for Nearest Neighbor Classifiers. Mach. Learn. 54(2): 125-152 (2004) - [c20]Saher Esmeir, Shaul Markovitch:
Lookahead-based algorithms for anytime induction of decision trees. ICML 2004 - [c19]Evgeniy Gabrilovich, Shaul Markovitch:
Text categorization with many redundant features: using aggressive feature selection to make SVMs competitive with C4.5. ICML 2004 - [c18]Dmitry Davidov, Evgeniy Gabrilovich, Shaul Markovitch:
Parameterized generation of labeled datasets for text categorization based on a hierarchical directory. SIGIR 2004: 250-257 - 2003
- [j16]Orna Grumberg, Shlomi Livne, Shaul Markovitch:
Learning to Order BDD Variables in Verification. J. Artif. Intell. Res. 18: 83-116 (2003) - [j15]Lev Finkelstein, Shaul Markovitch, Ehud Rivlin:
Optimal Schedules for Parallelizing Anytime Algorithms: The Case of Shared Resources. J. Artif. Intell. Res. 19: 73-138 (2003) - [j14]Shaul Markovitch, Asaf Shatil:
Speedup Learning for Repair-based Search by Identifying Redundant Steps. J. Mach. Learn. Res. 4: 649-682 (2003) - 2002
- [j13]Shaul Markovitch, Dan Rosenstein:
Feature Generation Using General Constructor Functions. Mach. Learn. 49(1): 59-98 (2002) - [c17]Dmitry Davidov, Shaul Markovitch:
Multiple-Goal Search Algorithms and their Application to Web Crawling. AAAI/IAAI 2002: 713-718 - [c16]Lev Finkelstein, Shaul Markovitch, Ehud Rivlin:
Optimal Schedules for Parallelizing Anytime Algorithms: The Case of Independent Processes. AAAI/IAAI 2002: 719-724 - 2001
- [j12]Lev Finkelstein, Shaul Markovitch:
Optimal schedules for monitoring anytime algorithms. Artif. Intell. 126(1-2): 63-108 (2001)
1990 – 1999
- 1999
- [j11]David Carmel, Shaul Markovitch:
Exploration Strategies for Model-based Learning in Multi-agent Systems: Exploration Strategies. Auton. Agents Multi Agent Syst. 2(2): 141-172 (1999) - [c15]Michael Lindenbaum, Shaul Markovitch, Dmitry Rusakov:
Selective Sampling for Nearest Neighbor Classifiers. AAAI/IAAI 1999: 366-371 - 1998
- [j10]David Carmel, Shaul Markovitch:
Pruning Algorithms for Multi-Model Adversary Search. Artif. Intell. 99(2): 325-355 (1998) - [j9]Lev Finkelstein, Shaul Markovitch:
Learning to Play Chess Selectively by Acquiring Move Patterns. J. Int. Comput. Games Assoc. 21(2): 100-119 (1998) - [j8]David Carmel, Shaul Markovitch:
Pruning Algorithms for Multi-model Adversary Search. J. Int. Comput. Games Assoc. 21(3): 193 (1998) - [j7]Lev Finkelstein, Shaul Markovitch:
A Selective Macro-learning Algorithm and its Application to the NxN Sliding-Tile Puzzle. J. Artif. Intell. Res. 8: 223-263 (1998) - [j6]Oleg Ledeniov, Shaul Markovitch:
The Divide-and-Conquer Subgoal-Ordering Algorithm for Speeding up Logic Inference. J. Artif. Intell. Res. 9: 37-97 (1998) - [j5]David Carmel, Shaul Markovitch:
Model-based learning of interaction strategies in multi-agent systems. J. Exp. Theor. Artif. Intell. 10(3): 309-332 (1998) - [c14]Oleg Ledeniov, Shaul Markovitch:
Learning Investment Functions for Controlling the Utility of Control Knowledge. AAAI/IAAI 1998: 463-468 - [c13]David Carmel, Shaul Markovitch:
How to Explore your Opponent's Strategy (almost) Optimally. ICMAS 1998: 64-71 - [i1]Lev Finkelstein, Shaul Markovitch:
A Selective Macro-learning Algorithm and its Application to the NxN Sliding-Tile Puzzle. CoRR cs.AI/9806102 (1998) - 1997
- [c12]David Carmel, Shaul Markovitch:
Exploration and Adaptation in Multiagent Systems: A Model-based Approach. IJCAI (1) 1997: 606-611 - 1996
- [j4]Shaul Markovitch, Yaron Sella:
Learning of Resource Allocation Strategies for Game Playing. Comput. Intell. 12: 88-105 (1996) - [c11]David Carmel, Shaul Markovitch:
Learning Models of Intelligent Agents. AAAI/IAAI, Vol. 1 1996: 62-67 - [c10]David Carmel, Shaul Markovitch:
Incorporating Opponent Models into Adversary Search. AAAI/IAAI, Vol. 1 1996: 120-125 - 1995
- [j3]Ido Dagan, Shaul Marcus, Shaul Markovitch:
Contextual word similarity and estimation from sparse data. Comput. Speech Lang. 9(2): 123-152 (1995) - [c9]David Carmel, Shaul Markovitch:
Opponent Modeling in Multi-Agent Systems. Adaption and Learning in Multi-Agent Systems 1995: 40-52 - 1993
- [j2]Shaul Markovitch, Paul D. Scott:
Information Filtering: Selection Mechanisms in Learning Systems. Mach. Learn. 10: 113-151 (1993) - [j1]Paul D. Scott, Shaul Markovitch:
Experience Selection and Problem Choice in an Exploratory Learning System. Mach. Learn. 12: 49-67 (1993) - [c8]Ido Dagan, Shaul Marcus, Shaul Markovitch:
Contextual Word Similarity and Estimation from Sparse Data. ACL 1993: 164-171 - [c7]Shaul Markovitch, Yaron Sella:
Learning of Resource Allocation Strategies for Game Playing. IJCAI 1993: 974-979
1980 – 1989
- 1989
- [b1]Shaul Markovitch:
Information filtering: Selection mechanisms in learning systems. University of Michigan, USA, 1989 - [c6]Paul D. Scott, Shaul Markovitch:
Uncertainty Based Selection of Learning Experiences. ML 1989: 358-361 - [c5]Shaul Markovitch, Paul D. Scott:
Information Filters and Their Implementation in the SYLLOG System. ML 1989: 404-407 - [c4]Paul D. Scott, Shaul Markovitch:
Learning Novel Domains Through Curiosity and Conjecture. IJCAI 1989: 669-674 - [c3]Shaul Markovitch, Paul D. Scott:
Utilization Filtering: A Method for Reducing the Inherent Harmfulness of Deductively Learned Knowledge. IJCAI 1989: 738-743 - [c2]Shaul Markovitch, Paul D. Scott:
Automatic Ordering of Subgoals - A Machine Learning Approach. NACLP 1989: 224-240 - 1988
- [c1]Shaul Markovitch, Paul D. Scott:
The Role of Forgetting in Learning. ML 1988: 459-465
Coauthor Index
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