Shaul Markovitch

发表

Opponent Modeling in Multi-Agent Systems

David Carmel, Shaul Markovitch, D. Carmel, 1995, Adaption and Learning in Multi-Agent Systems.

Parameterized generation of labeled datasets for text categorization based on a hierarchical directory

Evgeniy Gabrilovich, Shaul Markovitch, Dmitry Davidov, 2004, SIGIR '04.

Computing Semantic Relatedness Using Wikipedia-based Explicit Semantic Analysis

Evgeniy Gabrilovich, Shaul Markovitch, Shaul Markovitch, 2007, IJCAI.

Incorporating Opponent Models into Adversary Search

David Carmel, Shaul Markovitch, D. Carmel, 1996, AAAI/IAAI, Vol. 1.

The Role of Forgetting in Learning

Shaul Markovitch, Paul D. Scott, Shaul Markovitch, 1988, ML.

LEARNING OF RESOURCE ALLOCATION STRATEGIES FOR GAME PLAYING

Shaul Markovitch, Yaron Sella, Shaul Markovitch, 1993, IJCAI.

Learning Models of Intelligent Agents

David Carmel, Shaul Markovitch, D. Carmel, 1996, AAAI/IAAI, Vol. 1.

Experience Selection and Problem Choice in an Exploratory Learning System

Shaul Markovitch, P. Scott, 2004, Machine Learning.

Chapter 14 Representation Generation in an Exploratory Learning System

Shaul Markovitch, P. Scott, 2004 .

Applications of Macro Learning to Path Planning

S. Markovitch, Shaul Markovitch, 2004 .

Contextual Word Similarity and Estimation from Sparse Data

Ido Dagan, Shaul Markovitch, S. Marcus, 1993, ACL.

Computing semantic relatedness of words and texts in Wikipedia-derived semantic space

Shaul Markovitch, E. Gabrilovich, 2006 .

A game theory approach to the robot motion tracking problem

D. Carmel, Shaul Markovitch, 2006 .

Learning Causality from Textual Data

Shaul Markovitch, Kira Radinsky, S. Davidovich, 2011 .

Information Filtering: Selection Mechanisms in Learning Systems

Shaul Markovitch, P. Scott, 1993, Machine Learning.

Learning to bid in bridge

Shaul Markovitch, Asaf Amit, Shaul Markovitch, 2006, Machine Learning.

Self-consistent Batch-Classification

Shaul Markovitch, Oren Shnitzer, Shaul Markovitch, 2005 .

Anytime Generation of Counterfactual Explanations for Text Classification

Shaul Markovitch, Daniel Gilo, 2022, 2211.00369.

Interpreting Embedding Spaces by Conceptualization

Shaul Markovitch, Adi Simhi, 2022, ArXiv.

Learning and Exploiting Relative Weaknesses of Opponent Agents

Shaul Markovitch, Ronit Reger, Shaul Markovitch, 2005, Autonomous Agents and Multi-Agent Systems.

Exploration Strategies for Model-based Learning in Multi-agent Systems: Exploration Strategies

David Carmel, Shaul Markovitch, D. Carmel, 1999, Autonomous Agents and Multi-Agent Systems.

Model-based learning of interaction strategies in multi-agent systems

David Carmel, Shaul Markovitch, D. Carmel, 1998, J. Exp. Theor. Artif. Intell..

Selective Sampling for Nearest Neighbor Classifiers

Michael Lindenbaum, Shaul Markovitch, Dmitry M. Rusakov, 1999, Machine Learning.

How to explore your opponent's strategy (almost) optimally

David Carmel, Shaul Markovitch, D. Carmel, 1998, Proceedings International Conference on Multi Agent Systems (Cat. No.98EX160).

When Optimal Is Just Not Good Enough: Learning Fast Informative Action Cost Partitionings

Erez Karpas, Shaul Markovitch, Michael Katz, 2011, ICAPS.

Knowledge-Based Learning through Feature Generation pdf

Shaul Markovitch, Michal Badian, Shaul Markovitch, 2020, ArXiv.

Information Filtering: Selection Mechanisms in Learning Systems

Shaul Markovitch, Paul D. Scott, Shaul Markovitch, 1993, Machine Learning.

Learning to Predict from Textual Data

Shaul Markovitch, Kira Radinsky, Sagie Davidovich, 2012, J. Artif. Intell. Res..

Learning causality for news events prediction

Kira Radinsky, Sagie Davidovich, Shaul Markovitch, 2012, WWW.

Text categorization with many redundant features: using aggressive feature selection to make SVMs competitive with C4.5

Evgeniy Gabrilovich, Shaul Markovitch, Shaul Markovitch, 2004, ICML.

Wikipedia-based Semantic Interpretation for Natural Language Processing pdf

Evgeniy Gabrilovich, Shaul Markovitch, Shaul Markovitch, 2014, J. Artif. Intell. Res..

Feature Generation Using General Constructor Functions

Shaul Markovitch, Dan Rosenstein, Shaul Markovitch, 2002, Machine Learning.

Harnessing the Expertise of 70, 000 Human Editors: Knowledge-Based Feature Generation for Text Categorization

Evgeniy Gabrilovich, Shaul Markovitch, Shaul Markovitch, 2007, J. Mach. Learn. Res..

Textual Membership Queries pdf

Shaul Markovitch, Jonathan Zarecki, Shaul Markovitch, 2018, IJCAI.

Resource-Bounded Selection of Learning Algorithms

Shaul Markovitch, Assaf Glazer, 2013 .

Anytime learning of anycost classifiers

Shaul Markovitch, Saher Esmeir, Shaul Markovitch, 2011, Machine Learning.

Anytime Induction of Low-cost, Low-error Classifiers: a Sampling-based Approach

Shaul Markovitch, Saher Esmeir, Shaul Markovitch, 2008, J. Artif. Intell. Res..

Anytime Induction of Cost-sensitive Trees

Shaul Markovitch, Saher Esmeir, Shaul Markovitch, 2007, NIPS.

Anytime Learning of Decision Trees

Shaul Markovitch, Saher Esmeir, Shaul Markovitch, 2007, J. Mach. Learn. Res..

When a Decision Tree Learner Has Plenty of Time

Shaul Markovitch, Saher Esmeir, Shaul Markovitch, 2006, AAAI.

Multiple-Goal Heuristic Search pdf

Shaul Markovitch, Dmitry Davidov, Shaul Markovitch, 2006, J. Artif. Intell. Res..

Interruptible anytime algorithms for iterative improvement of decision trees

Shaul Markovitch, Saher Esmeir, Shaul Markovitch, 2005, UBDM '05.

Lookahead-based algorithms for anytime induction of decision trees

Shaul Markovitch, Saher Esmeir, Shaul Markovitch, 2004, ICML.

Optimal Schedules for Parallelizing Anytime Algorithms: The Case of Shared Resources

Ehud Rivlin, Shaul Markovitch, Lev Finkelstein, 2003, J. Artif. Intell. Res..

Optimal schedules for parallelizing anytime algorithms: the case of independent processes

Ehud Rivlin, Shaul Markovitch, Lev Finkelstein, 2002, AAAI/IAAI.

Optimal schedules for monitoring anytime algorithms

Shaul Markovitch, Lev Finkelstein, Shaul Markovitch, 2001, Artif. Intell..

Optimal Schedules for Parallelizing Anytime Algorithms

Ehud Rivlin, Shaul Markovitch, Lev Finkelstein, 2001 .

Named Entity Disambiguation for Noisy Text pdf

Omer Levy, Ikuya Yamada, Shaul Markovitch, 2017, CoNLL.

Interpreting Embedding Spaces by Conceptualization

Shaul Markovitch, Adi Simhi, 2022, EMNLP.

Online Speedup Learning for Optimal Planning

Erez Karpas, Carmel Domshlak, Shaul Markovitch, 2012, J. Artif. Intell. Res..

Occam's Razor Just Got Sharper

Shaul Markovitch, Saher Esmeir, Shaul Markovitch, 2007, IJCAI.

Active Learning with Near Misses

Ehud Rivlin, Shaul Markovitch, Nela Gurevich, 2006, AAAI.

Concept-Based Information Retrieval Using Explicit Semantic Analysis

Evgeniy Gabrilovich, Shaul Markovitch, Ofer Egozi, 2011, TOIS.

Learning to Order BDD Variables in Verification pdf

Shaul Markovitch, Orna Grumberg, Shlomi Livne, 2011, J. Artif. Intell. Res..

The Divide-and-Conquer Subgoal-Ordering Algorithm for Speeding up Logic Inference

Shaul Markovitch, Oleg Ledeniov, Shaul Markovitch, 1998, J. Artif. Intell. Res..

Exploration and Adaptation in Multiagent Systems: A Model-based Approach

David Carmel, Shaul Markovitch, D. Carmel, 1997, IJCAI.

Learning High-Density Regions for a Generalized Kolmogorov-Smirnov Test in High-Dimensional Data

Michael Lindenbaum, Shaul Markovitch, Assaf Glazer, 2012, NIPS.

Pruning Algorithms for Multi-Model Adversary Search

David Carmel, Shaul Markovitch, D. Carmel, 1998, Artif. Intell..

Representation generation in an exploratory learning system

Shaul Markovitch, Paul D. Scott, Shaul Markovitch, 1991 .

Learning to Play Chess Selectively by Acquiring Move Patterns

Shaul Markovitch, Lev Finkelstein, Shaul Markovitch, 1998, J. Int. Comput. Games Assoc..

Learning and using opponent models in adversary search

David Carmel, Shaul Markovitch, D. Carmel, 1996 .

Overcoming the Brittleness Bottleneck using Wikipedia: Enhancing Text Categorization with Encyclopedic Knowledge

Evgeniy Gabrilovich, Shaul Markovitch, Shaul Markovitch, 2006, AAAI.

Feature Generation for Text Categorization Using World Knowledge

Evgeniy Gabrilovich, Shaul Markovitch, Shaul Markovitch, 2005, IJCAI.

Utilization Filtering of Macros Based on Goal Similarity

Shaul Markovitch, Uri Keidar, E. Webman, 2004 .

Systematic Experimentation with Deductive Learning: Satisficing vs. Optimizing search

Shaul Markovitch, Shaul Markovitch, 2004 .

A word at a time: computing word relatedness using temporal semantic analysis

Evgeniy Gabrilovich, Eugene Agichtein, Kira Radinsky, 2011, WWW.

Algorithmic Exam Generation

Shaul Markovitch, Omer Geiger, Shaul Markovitch, 2015, IJCAI.

A Two-Stage Masked LM Method for Term Set Expansion

Yoav Goldberg, Shaul Markovitch, Guy Kushilevitz, 2020, ACL.

Learning Implicit Transfer for Person Re-identification

Michael Lindenbaum, Shaul Markovitch, Tamar Avraham, 2012, ECCV Workshops.

Prototype-Based Chemical Design using Diversity-Driven Generative Models

Shaul Markovitch, Shahar Harel, Kira Radinsky, 2019 .

Learning Causality from Textual Data

Shaul Markovitch, Kira Radinsky, S. Davidovich, 2011 .

Automatic Generation of Language-Independent Features for Cross-Lingual Classification

Shaul Markovitch, Sarai Duek, 2018, ArXiv.

One-Class Background Model

Michael Lindenbaum, Shaul Markovitch, Assaf Glazer, 2012, ACCV Workshops.

Predicting the News of Tomorrow Using Patterns in Web Search Queries

Shaul Markovitch, Kira Radinsky, S. Davidovich, 2008, 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology.

Speedup Learning for Repair-based Search by Identifying Redundant Steps

Shaul Markovitch, Asaf Shatil, Shaul Markovitch, 2003, J. Mach. Learn. Res..

To Max or Not to Max: Online Learning for Speeding Up Optimal Planning

Erez Karpas, Carmel Domshlak, Shaul Markovitch, 2010, AAAI.

Approximating Hierarchical MV-sets for Hierarchical Clustering

Michael Lindenbaum, Shaul Markovitch, Omer Weissbrod, 2014, NIPS.

Computing semantic relatedness of words and texts in Wikipedia-derived semantic space

Shaul Markovitch, E. Gabrilovich, 2006 .

Wikipedia-based Semantic Interpretation for Natural Language Processing

Shaul Markovitch, E. Gabrilovich, 2009, J. Artif. Intell. Res..

Max-Prob: An Unbiased Rational Decision Making Procedure for Multiple-Adversary Environments

Shaul Markovitch, Anat Hashavit, Shaul Markovitch, 2011, IJCAI.

Concept-Based Feature Generation and Selection for Information Retrieval

Evgeniy Gabrilovich, Shaul Markovitch, Ofer Egozi, 2008, AAAI.

A General Search-Based Framework for Generating Textual Counterfactual Explanations

Shaul Markovitch, Daniel Gilo, 2022, AAAI.

The SelMax Planner: Online Learning for Speeding up Optimal Planning

Erez Karpas, Carmel Domshlak, Malte Helmert, 2010 .