| 2025 | AIES | What's Individual About Individual Fairness? | Shai Ben-David, Pascale Gourdeau, Tosca Lechner, Ruth Urner |
| 2024 | COLT | Inherent limitations of dimensions for characterizing learnability of distribution classes. | Tosca Lechner, Shai Ben-David |
| 2023 | ALT | On Computable Online Learning. | Niki Hasrati, Shai Ben-David |
| 2023 | ICML | Strategic Classification with Unknown User Manipulations. | Tosca Lechner, Ruth Urner, Shai Ben-David |
| 2021 | AAAI | Classification Confidence Scores with Point-wise Guarantees. | Nivasini Ananthakrishnan, Shai Ben-David, Tosca Lechner |
| 2021 | COLT | Open Problem: Are all VC-classes CPAC learnable? | Sushant Agarwal, Nivasini Ananthakrishnan, Shai Ben-David, Tosca Lechner, Ruth Urner |
| 2021 | STOC | Learnability can be independent of set theory (invited paper). | Shai Ben-David, Pavel Hrubes, Shay Moran, Amir Shpilka, Amir Yehudayoff |
| 2021 | UAI | Identifying regions of trusted predictions. | Nivasini Ananthakrishnan, Shai Ben-David, Tosca Lechner, Ruth Urner |
| 2020 | ALT | On Learnability wih Computable Learners. | Sushant Agarwal, Nivasini Ananthakrishnan, Shai Ben-David, Tosca Lechner, Ruth Urner |
| 2019 | AISTATS | Semi-supervised clustering for de-duplication. | Shrinu Kushagra, Shai Ben-David, Ihab F. Ilyas |
| 2019 | COLT | When can unlabeled data improve the learning rate? | Christina Gpfert, Shai Ben-David, Olivier Bousquet, Sylvain Gelly, Ilya O. Tolstikhin, Ruth Urner |
| 2019 | ICDE | A Semi-Supervised Framework of Clustering Selection for De-Duplication. | Shrinu Kushagra, Hemant Saxena, Ihab F. Ilyas, Shai Ben-David |
| 2018 | AAAI | Sample-Efficient Learning of Mixtures. | Hassan Ashtiani, Shai Ben-David, Abbas Mehrabian |
| 2018 | AAAI | Clustering - What Both Theoreticians and Practitioners Are Doing Wrong. | Shai Ben-David |
| 2018 | ALT | Multi-task {K}ernel {L}earning Based on {P}robabilistic {L}ipschitzness. | Anastasia Pentina, Shai Ben-David |
| 2016 | ALT | On Version Space Compression. | Shai Ben-David, Ruth Urner |
| 2016 | ALT | Finding Meaningful Cluster Structure Amidst Background Noise. | Shrinu Kushagra, Samira Samadi, Shai Ben-David |
| 2016 | MFCS | How Far Are We From Having a Satisfactory Theory of Clustering? | Shai Ben-David |
| 2015 | ALT | Information Preserving Dimensionality Reduction. | Shrinu Kushagra, Shai Ben-David |
| 2015 | ALT | Multi-task and Lifelong Learning of Kernels. | Anastasia Pentina, Shai Ben-David |
| 2015 | COLT | Hierarchical Label Queries with Data-Dependent Partitions. | Samory Kpotufe, Ruth Urner, Shai Ben-David |
| 2015 | UAI | Representation Learning for Clustering: A Statistical Framework. | Hassan Ashtiani, Shai Ben-David |
| 2014 | COLT | The sample complexity of agnostic learning under deterministic labels. | Shai Ben-David, Ruth Urner |
| 2014 | ICML | Clustering in the Presence of Background Noise. | Shai Ben-David, Nika Haghtalab |
| 2014 | ISAIM | The sample complexity of agnostic learning with deterministic labels. | Shai Ben-David, Ruth Urner |
| 2013 | AISTATS | Clustering Oligarchies. | Margareta Ackerman, Shai Ben-David, David Loker, Sivan Sabato |
| 2013 | COLT | PLAL: Cluster-based active learning. | Ruth Urner, Sharon Wulff, Shai Ben-David |
| 2013 | ICML | Monochromatic Bi-Clustering. | Sharon Wulff, Ruth Urner, Shai Ben-David |
| 2012 | AAAI | Weighted Clustering. | Margareta Ackerman, Shai Ben-David, Simina Brnzei, David Loker |
| 2012 | ALT | On the Hardness of Domain Adaptation and the Utility of Unlabeled Target Samples. | Shai Ben-David, Ruth Urner |
| 2012 | ICML | Minimizing The Misclassification Error Rate Using a Surrogate Convex Loss. | Shai Ben-David, David Loker, Nathan Srebro, Karthik Sridharan |
| 2012 | ISAIM | Domain Adaptation--Can Quantity compensate for Quality?. | Shai Ben-David, Shai Shalev-Shwartz, Ruth Urner |
| 2011 | ALT | Learning a Classifier when the Labeling Is Known. | Shalev Ben-David, Shai Ben-David |
| 2011 | ICML | Access to Unlabeled Data can Speed up Prediction Time. | Ruth Urner, Shai Shalev-Shwartz, Shai Ben-David |
| 2011 | IJCAI | Discerning Linkage-Based Algorithms among Hierarchical Clustering Methods. | Margareta Ackerman, Shai Ben-David |
| 2010 | COLT | Characterization of Linkage-based Clustering. | Margareta Ackerman, Shai Ben-David, David Loker |
| 2010 | ICDE | ProbClean: A probabilistic duplicate detection system. | George Beskales, Mohamed A. Soliman, Ihab F. Ilyas, Shai Ben-David, Yubin Kim |
| 2009 | COLT | Agnostic Online Learning. | Shai Ben-David, Dvid Pl, Shai Shalev-Shwartz |
| 2009 | Interspeech | RTTS: towards enterprise-level real-time speech transcription and translation services. | Juan M. Huerta, Cheng Wu, Andrej Sakrajda, Sasha Caskey, Ea-Ee Jan, Alexander Faisman, Shai Ben-David, Wen Liu, Antonio Lee, Osamuyimen Stewart, Michael Frissora, David M. Lubensky |
| 2009 | UAI | A Uniqueness Theorem for Clustering. | Reza Zadeh, Shai Ben-David |
| 2008 | COLT | Relating Clustering Stability to Properties of Cluster Boundaries. | Shai Ben-David, Ulrike von Luxburg |
| 2008 | COLT | Does Unlabeled Data Provably Help? Worst-case Analysis of the Sample Complexity of Semi-Supervised Learning. | Shai Ben-David, Tyler Lu, Dvid Pl |
| 2007 | COLT | Stability of | Shai Ben-David, Dvid Pl, Hans Ulrich Simon |
| 2006 | COLT | A Sober Look at Clustering Stability. | Shai Ben-David, Ulrike von Luxburg, Dvid Pl |
| 2006 | COLT | Learning Bounds for Support Vector Machines with Learned Kernels. | Nathan Srebro, Shai Ben-David |
| 2006 | TAMC | Alternative Measures of Computational Complexity with Applications to Agnostic Learning. | Shai Ben-David |
| 2005 | ICASSP | Nonparametric change detection in 2D random sensor field. | Ting He, Shai Ben-David, Lang Tong |
| 2004 | COLT | A Framework for Statistical Clustering with a Constant Time Approximation Algorithms for K-Median Clustering. | Shai Ben-David |
| 2004 | VLDB | Detecting Change in Data Streams. | Daniel Kifer, Shai Ben-David, Johannes Gehrke |
| 2003 | COLT | Exploiting Task Relatedness for Mulitple Task Learning. | Shai Ben-David, Reba Schuller |
| 2002 | KDD | A theoretical framework for learning from a pool of disparate data sources. | Shai Ben-David, Johannes Gehrke, Reba Schuller |
| 2001 | COLT | Limitations of Learning via Embeddings in Euclidean Half-Spaces. | Shai Ben-David, Nadav Eiron, Hans Ulrich Simon |
| 2001 | COLT | Agnostic Boosting. | Shai Ben-David, Philip M. Long, Yishay Mansour |
| 2000 | COLT | On the Difficulty of Approximately Maximizing Agreements. | Shai Ben-David, Nadav Eiron, Philip M. Long |
| 2000 | COLT | The Computational Complexity of Densest Region Detection. | Shai Ben-David, Nadav Eiron, Hans Ulrich Simon |
| 2000 | COLT | Localized Boosting. | Ron Meir, Ran El-Yaniv, Shai Ben-David |
| 1997 | STOC | A Composition Theorem for Learning Algorithms with Applications to Geometric Concept Classes. | Shai Ben-David, Nader H. Bshouty, Eyal Kushilevitz |
| 1996 | COLT | Learning Changing Concepts by Exploiting the Structure of Change. | Peter L. Bartlett, Shai Ben-David, Sanjeev R. Kulkarni |
| 1995 | COLT | On Self-Directed Learning. | Shai Ben-David, Nadav Eiron, Eyal Kushilevitz |
| 1995 | COLT | A Note on VC-Dimension and Measures of Sets of Reals. | Shai Ben-David, Leonid Gurvits |
| 1994 | AAAI | Applying VC-Dimension Analysis To 3D Object Recognition from Perspective Projections. | Michael Lindenbaum, Shai Ben-David |
| 1994 | ALT | Learnability with Restricted Focus of Attention guarantees Noise-Tolerance. | Shai Ben-David, Eli Dichterman |
| 1994 | ECCV | Applying VC-dimension Analysis To Object Recognition. | Michael Lindenbaum, Shai Ben-David |
| 1994 | LICS | a modal logic for subjective default reasoning | Shai Ben-David, Rachel Ben-Eliyahu |
| 1993 | COLT | Learning with Restricted Focus of Attention. | Shai Ben-David, Eli Dichterman |
| 1993 | COLT | On Learning in the Limit and Non-Uniform (epsilon, delta)-Learning. | Shai Ben-David, Michal Jacovi |
| 1993 | COLT | Localization vs. Identification of Semi-Algebraic Sets. | Shai Ben-David, Michael Lindenbaum |
| 1993 | FOCS | Scale-sensitive Dimensions, Uniform Convergence, and Learnability | Noga Alon, Shai Ben-David, Nicol Cesa-Bianchi, David Haussler |
| 1992 | COLT | Characterizations of Learnability for Classes of { | Shai Ben-David, Nicol Cesa-Bianchi, Philip M. Long |
| 1992 | STOC | Can Finite Samples Detect Singularities of Real-Valued Functions? | Shai Ben-David |
| 1990 | COLT | Learning by Distances. | Shai Ben-David, Alon Itai, Eyal Kushilevitz |
| 1990 | STOC | On the Power of Randomization in Online Algorithms (Extended Abstract) | Shai Ben-David, Allan Borodin, Richard M. Karp, Gbor Tardos, Avi Wigderson |
| 1989 | COLT | A Parametrization Scheme for Classifying Models of Learnability. | Shai Ben-David, Gyora M. Benedek, Yishay Mansour |
| 1989 | STOC | On the Theory of Average Case Complexity | Shai Ben-David, Benny Chor, Oded Goldreich, Michael Luby |
| 1988 | PODC | The Global Time Assumption and Semantics for Concurrent Systems. | Shai Ben-David |