Ben-David, Shai

83 publications

NeurIPS 2025 Learning from Positive and Unlabeled Examples -Finite Size Sample Bounds Farnam Mansouri, Shai Ben-David

TMLR 2024 Continual Learning: Applications and the Road Forward Eli Verwimp, Rahaf Aljundi, Shai Ben-David, Matthias Bethge, Andrea Cossu, Alexander Gepperth, Tyler L. Hayes, Eyke Hüllermeier, Christopher Kanan, Dhireesha Kudithipudi, Christoph H. Lampert, Martin Mundt, Razvan Pascanu, Adrian Popescu, Andreas S. Tolias, Joost van de Weijer, Bing Liu, Vincenzo Lomonaco, Tinne Tuytelaars, Gido M van de Ven

COLT 2024 Inherent Limitations of Dimensions for Characterizing Learnability of Distribution Classes Tosca Lechner, Shai Ben-David

NeurIPS 2023 Distribution Learnability and Robustness Shai Ben-David, Alex Bie, Gautam Kamath, Tosca Lechner

ALT 2023 On Computable Online Learning Niki Hasrati, Shai Ben-David

NeurIPS 2023 Private Distribution Learning with Public Data: The View from Sample Compression Shai Ben-David, Alex Bie, Clément L Canonne, Gautam Kamath, Vikrant Singhal

ICML 2023 Strategic Classification with Unknown User Manipulations Tosca Lechner, Ruth Urner, Shai Ben-David

CoLLAs 2022 Inherent Limitations of Multi-Task Fair Representations Tosca Lechner, Shai Ben-David

UAI 2021 Identifying Regions of Trusted Predictions Nivasini Ananthakrishnan, Shai Ben-David, Tosca Lechner, Ruth Urner

COLT 2021 Open Problem: Are All VC-Classes CPAC Learnable? Sushant Agarwal, Nivasini Ananthakrishnan, Shai Ben-David, Tosca Lechner, Ruth Urner

ALT 2020 On Learnability Wih Computable Learners Sushant Agarwal, Nivasini Ananthakrishnan, Shai Ben-David, Tosca Lechner, Ruth Urner

AISTATS 2019 Semi-Supervised Clustering for De-Duplication Shrinu Kushagra, Shai Ben-David, Ihab Ilyas

COLT 2019 When Can Unlabeled Data Improve the Learning Rate? Christina Göpfert, Shai Ben-David, Olivier Bousquet, Sylvain Gelly, Ilya Tolstikhin, Ruth Urner

AAAI 2018 Clustering - What Both Theoreticians and Practitioners Are Doing Wrong Shai Ben-David

NeurIPS 2018 Empirical Risk Minimization Under Fairness Constraints Michele Donini, Luca Oneto, Shai Ben-David, John S Shawe-Taylor, Massimiliano Pontil

ALT 2018 Multi-Task Kernel Learning Based on Probabilistic Lipschitzness Anastasia Pentina, Shai Ben-David

NeurIPS 2018 Nearly Tight Sample Complexity Bounds for Learning Mixtures of Gaussians via Sample Compression Schemes Hassan Ashtiani, Shai Ben-David, Nicholas Harvey, Christopher Liaw, Abbas Mehrabian, Yaniv Plan

AAAI 2018 Sample-Efficient Learning of Mixtures Hassan Ashtiani, Shai Ben-David, Abbas Mehrabian

JMLR 2016 A Characterization of Linkage-Based Hierarchical Clustering Margareta Ackerman, Shai Ben-David

NeurIPS 2016 Clustering with Same-Cluster Queries Hassan Ashtiani, Shrinu Kushagra, Shai Ben-David

ALT 2016 Finding Meaningful Cluster Structure Amidst Background Noise Shrinu Kushagra, Samira Samadi, Shai Ben-David

ALT 2016 On Version Space Compression Shai Ben-David, Ruth Urner

COLT 2015 Hierarchical Label Queries with Data-Dependent Partitions Samory Kpotufe, Ruth Urner, Shai Ben-David

ALT 2015 Information Preserving Dimensionality Reduction Shrinu Kushagra, Shai Ben-David

ALT 2015 Multi-Task and Lifelong Learning of Kernels Anastasia Pentina, Shai Ben-David

JMLR 2015 Multiclass Learnability and the ERM Principle Amit Daniely, Sivan Sabato, Shai Ben-David, Shai Shalev-Shwartz

UAI 2015 Representation Learning for Clustering: A Statistical Framework Hassan Ashtiani, Shai Ben-David

ICML 2014 Clustering in the Presence of Background Noise Shai Ben-David, Nika Haghtalab

COLT 2014 The Sample Complexity of Agnostic Learning Under Deterministic Labels Shai Ben-David, Ruth Urner

AISTATS 2013 Clustering Oligarchies Margareta Ackerman, Shai Ben-David, David Loker, Sivan Sabato

ICML 2013 Monochromatic Bi-Clustering Sharon Wulff, Ruth Urner, Shai Ben-David

COLT 2013 PLAL: Cluster-Based Active Learning Ruth Urner, Sharon Wulff, Shai Ben-David

ICML 2012 Minimizing the Misclassification Error Rate Using a Surrogate Convex Loss Shai Ben-David, David Loker, Nathan Srebro, Karthik Sridharan

ALT 2012 On the Hardness of Domain Adaptation and the Utility of Unlabeled Target Samples Shai Ben-David, Ruth Urner

AAAI 2012 Weighted Clustering Margareta Ackerman, Shai Ben-David, Simina Brânzei, David Loker

ICML 2011 Access to Unlabeled Data Can Speed up Prediction Time Ruth Urner, Shai Shalev-Shwartz, Shai Ben-David

IJCAI 2011 Discerning Linkage-Based Algorithms Among Hierarchical Clustering Methods Margareta Ackerman, Shai Ben-David

ALT 2011 Learning a Classifier When the Labeling Is Known Shalev Ben-David, Shai Ben-David

COLT 2011 Multiclass Learnability and the ERM Principle Amit Daniely, Sivan Sabato, Shai Ben-David, Shai Shalev-Shwartz

MLJ 2010 A Theory of Learning from Different Domains Shai Ben-David, John Blitzer, Koby Crammer, Alex Kulesza, Fernando Pereira, Jennifer Wortman Vaughan

COLT 2010 Characterization of Linkage-Based Clustering Margareta Ackerman, Shai Ben-David, David Loker

NeurIPS 2010 Towards Property-Based Classification of Clustering Paradigms Margareta Ackerman, Shai Ben-David, David Loker

UAI 2009 A Uniqueness Theorem for Clustering Reza Zadeh, Shai Ben-David

COLT 2009 Agnostic Online Learning Shai Ben-David, Dávid Pál, Shai Shalev-Shwartz

AISTATS 2009 Clusterability: A Theoretical Study Margareta Ackerman, Shai Ben-David

AISTATS 2009 Learning Low Density Separators Shai Ben-David, Tyler Lu, David Pal, Miroslava Sotakova

ECML-PKDD 2009 Theory-Practice Interplay in Machine Learning - Emerging Theoretical Challenges Shai Ben-David

MLJ 2008 A Notion of Task Relatedness Yielding Provable Multiple-Task Learning Guarantees Shai Ben-David, Reba Schuller Borbely

COLT 2008 Does Unlabeled Data Provably Help? Worst-Case Analysis of the Sample Complexity of Semi-Supervised Learning Shai Ben-David, Tyler Lu, Dávid Pál

NeurIPS 2008 Measures of Clustering Quality: A Working Set of Axioms for Clustering Shai Ben-David, Margareta Ackerman

COLT 2008 Relating Clustering Stability to Properties of Cluster Boundaries Shai Ben-David, Ulrike von Luxburg

MLJ 2007 A Framework for Statistical Clustering with Constant Time Approximation Algorithms for K-Median and K-Means Clustering Shai Ben-David

COLT 2007 Stability of K -Means Clustering Shai Ben-David, Dávid Pál, Hans Ulrich Simon

COLT 2006 A Sober Look at Clustering Stability Shai Ben-David, Ulrike von Luxburg, Dávid Pál

NeurIPS 2006 Analysis of Representations for Domain Adaptation Shai Ben-David, John Blitzer, Koby Crammer, Fernando Pereira

COLT 2006 Learning Bounds for Support Vector Machines with Learned Kernels Nathan Srebro, Shai Ben-David

COLT 2004 A Framework for Statistical Clustering with a Constant Time Approximation Algorithms for K-Median Clustering Shai Ben-David

ALT 2004 Algorithmic Learning Theory, 15th International Conference, ALT 2004, Padova, Italy, October 2-5, 2004, Proceedings Shai Ben-David, John Case, Akira Maruoka

COLT 2003 Exploiting Task Relatedness for Mulitple Task Learning Shai Ben-David, Reba Schuller

JMLR 2002 Limitations of Learning via Embeddings in Euclidean Half Spaces Shai Ben-David, Nadav Eiron, Hans Ulrich Simon

COLT 2001 Agnostic Boosting Shai Ben-David, Philip M. Long, Yishay Mansour

COLT 2001 Limitations of Learning via Embeddings in Euclidean Half-Spaces Shai Ben-David, Nadav Eiron, Hans Ulrich Simon

NeurIPS 2000 Efficient Learning of Linear Perceptrons Shai Ben-David, Hans-Ulrich Simon

MLJ 2000 Learning Changing Concepts by Exploiting the Structure of Change Peter L. Bartlett, Shai Ben-David, Sanjeev R. Kulkarni

COLT 2000 Localized Boosting Ron Meir, Ran El-Yaniv, Shai Ben-David

COLT 2000 On the Difficulty of Approximately Maximizing Agreements Shai Ben-David, Nadav Eiron, Philip M. Long

COLT 2000 The Computational Complexity of Densest Region Detection Shai Ben-David, Nadav Eiron, Hans Ulrich Simon

COLT 1999 Proceedings of the Twelfth Annual Conference on Computational Learning Theory, COLT 1999, Santa Cruz, CA, USA, July 7-9, 1999 Shai Ben-David, Philip M. Long

MLJ 1998 Localization vs. Identification of Semi-Algebraic Sets Shai Ben-David, Michael Lindenbaum

MLJ 1998 Self-Directed Learning and Its Relation to the VC-Dimension and to Teacher-Directed Learning Shai Ben-David, Nadav Eiron

MLJ 1997 Online Learning Versus Offline Learning Shai Ben-David, Eyal Kushilevitz, Yishay Mansour

COLT 1996 Learning Changing Concepts by Exploiting the Structure of Change Peter L. Bartlett, Shai Ben-David, Sanjeev R. Kulkarni

COLT 1995 A Note on VC-Dimension and Measures of Sets of Reals Shai Ben-David, Leonid Gurvits

COLT 1995 On Self-Directed Learning Shai Ben-David, Nadav Eiron, Eyal Kushilevitz

AAAI 1994 Applying VC-Dimension Analysis to 3D Object Recognition from Perspective Projections Michael Lindenbaum, Shai Ben-David

ECCV 1994 Applying VC-Dimension Analysis to Object Recognition Michael Lindenbaum, Shai Ben-David

ALT 1994 Learnability with Restricted Focus of Attention Guarantees Noise-Tolerance Shai Ben-David, Eli Dichterman

COLT 1993 Learning with Restricted Focus of Attention Shai Ben-David, Eli Dichterman

COLT 1993 Localization vs. Identification of Semi-Algebraic Sets Shai Ben-David, Michael Lindenbaum

COLT 1993 On Learning in the Limit and Non-Uniform (epsilon, Delta)-Learning Shai Ben-David, Michal Jacovi

COLT 1992 Characterizations of Learnability for Classes of O, ..., N-Valued Functions Shai Ben-David, Nicolò Cesa-Bianchi, Philip M. Long

COLT 1990 Learning by Distances Shai Ben-David, Alon Itai, Eyal Kushilevitz

COLT 1989 A Parametrization Scheme for Classifying Models of Learnability Shai Ben-David, Gyora M. Benedek, Yishay Mansour