Shai Ben-David

Publication Activity (10 Years)

Years Active: 1986-2023
Publications (10 Years): 53

Top Venues

CoRR

ALT

COLT

NeurIPS

Publications

Eli Verwimp, 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
Continual Learning: Applications and the Road Forward. CoRR (2023)
Niki Hasrati, Shai Ben-David
On Computable Online Learning. CoRR (2023)
Shai Ben-David, Alex Bie, Gautam Kamath, Tosca Lechner
Distribution Learnability and Robustness. NeurIPS (2023)
Tosca Lechner, Shai Ben-David
Impossibility of Characterizing Distribution Learning - a simple solution to a long-standing problem. CoRR (2023)
Shai Ben-David, Alex Bie, Clément L. Canonne, Gautam Kamath, Vikrant Singhal
Private Distribution Learning with Public Data: The View from Sample Compression. CoRR (2023)
Tosca Lechner, Ruth Urner, Shai Ben-David
Strategic Classification with Unknown User Manipulations. ICML (2023)
Niki Hasrati, Shai Ben-David
On Computable Online Learning. ALT (2023)
Shai Ben-David, Alex Bie, Clément L. Canonne, Gautam Kamath, Vikrant Singhal
Private Distribution Learning with Public Data: The View from Sample Compression. NeurIPS (2023)
Tosca Lechner, Shai Ben-David
Inherent Limitations of Multi-Task Fair Representations. CoLLAs (2022)
Sushant Agarwal, Nivasini Ananthakrishnan, Shai Ben-David, Tosca Lechner, Ruth Urner
Open Problem: Are all VC-classes CPAC learnable? COLT (2021)
Nivasini Ananthakrishnan, Shai Ben-David, Tosca Lechner
Classification Confidence Scores with Point-wise Guarantees. SafeAI@AAAI (2021)
Nivasini Ananthakrishnan, Shai Ben-David, Tosca Lechner, Ruth Urner
Identifying regions of trusted predictions. UAI (2021)
Margareta Ackerman, Shai Ben-David, Simina Brânzei, David Loker
Weighted clustering: Towards solving the user's dilemma. Pattern Recognit. 120 (2021)
Shai Ben-David, Pavel Hrubes, Shay Moran, Amir Shpilka, Amir Yehudayoff
Learnability can be independent of set theory (invited paper). STOC (2021)
Tosca Lechner, Shai Ben-David, Sushant Agarwal, Nivasini Ananthakrishnan
Impossibility results for fair representations. CoRR (2021)
Gintare Karolina Dziugaite, Shai Ben-David, Daniel M. Roy
Enforcing Interpretability and its Statistical Impacts: Trade-offs between Accuracy and Interpretability. CoRR (2020)
Sushant Agarwal, Nivasini Ananthakrishnan, Shai Ben-David, Tosca Lechner, Ruth Urner
On Learnability wih Computable Learners. ALT (2020)
Hassan Ashtiani, Shai Ben-David, Nicholas J. A. Harvey, Christopher Liaw, Abbas Mehrabian, Yaniv Plan
Near-optimal Sample Complexity Bounds for Robust Learning of Gaussian Mixtures via Compression Schemes. J. ACM 67 (6) (2020)
Shai Ben-David, Pavel Hrubes, Shay Moran, Amir Shpilka, Amir Yehudayoff
Learnability can be undecidable. Nat. Mach. Intell. 1 (1) (2019)
Shrinu Kushagra, Shai Ben-David, Ihab F. Ilyas
Semi-supervised clustering for de-duplication. AISTATS (2019)
Shai Ben-David, Pavel Hrubes, Shay Moran, Amir Shpilka, Amir Yehudayoff
Author Correction: Learnability can be undecidable. Nat. Mach. Intell. 1 (2) (2019)
Christina Göpfert, Shai Ben-David, Olivier Bousquet, Sylvain Gelly, Ilya O. Tolstikhin, Ruth Urner
When can unlabeled data improve the learning rate? CoRR (2019)
Shrinu Kushagra, Hemant Saxena, Ihab F. Ilyas, Shai Ben-David
A Semi-Supervised Framework of Clustering Selection for De-Duplication. ICDE (2019)
Christina Göpfert, Shai Ben-David, Olivier Bousquet, Sylvain Gelly, Ilya O. Tolstikhin, Ruth Urner
When can unlabeled data improve the learning rate? COLT (2019)
Michele Donini, Luca Oneto, Shai Ben-David, John Shawe-Taylor, Massimiliano Pontil
Empirical Risk Minimization Under Fairness Constraints. NeurIPS (2018)
Shai Ben-David
Clustering - What Both Theoreticians and Practitioners are Doing Wrong. CoRR (2018)
Shrinu Kushagra, Shai Ben-David, Ihab F. Ilyas
Semi-supervised clustering for de-duplication. CoRR (2018)
Hassan Ashtiani, Shai Ben-David, Abbas Mehrabian
Sample-Efficient Learning of Mixtures. AAAI (2018)
Hassan Ashtiani, Shai Ben-David, Nicholas J. A. Harvey, Christopher Liaw, Abbas Mehrabian, Yaniv Plan
Nearly tight sample complexity bounds for learning mixtures of Gaussians via sample compression schemes. NeurIPS (2018)
Michele Donini, Luca Oneto, Shai Ben-David, John Shawe-Taylor, Massimiliano Pontil
Empirical Risk Minimization under Fairness Constraints. CoRR (2018)
Anastasia Pentina, Shai Ben-David
Multi-task {K}ernel {L}earning Based on {P}robabilistic {L}ipschitzness. ALT (2018)
Shai Ben-David
Clustering - What Both Theoreticians and Practitioners Are Doing Wrong. AAAI (2018)
Hassan Ashtiani, Shai Ben-David, Abbas Mehrabian
Sample-Efficient Learning of Mixtures. CoRR (2017)
Hassan Ashtiani, Shai Ben-David, Abbas Mehrabian
Agnostic Distribution Learning via Compression. CoRR (2017)
Shrinu Kushagra, Nicole McNabb, Yaoliang Yu, Shai Ben-David
Provably noise-robust, regularised k-means clustering. CoRR (2017)
Shai Ben-David, Pavel Hrubes, Shay Moran, Amir Shpilka, Amir Yehudayoff
A learning problem that is independent of the set theory ZFC axioms. CoRR (2017)
Shai Ben-David, Ruth Urner
On Version Space Compression. ALT (2016)
Hassan Ashtiani, Shrinu Kushagra, Shai Ben-David
Clustering with Same-Cluster Queries. NIPS (2016)
Maria-Florina Balcan, Shai Ben-David, Ruth Urner, Ulrike von Luxburg
Foundations of Unsupervised Learning (Dagstuhl Seminar 16382). Dagstuhl Reports 6 (9) (2016)
Margareta Ackerman, Shai Ben-David
A Characterization of Linkage-Based Hierarchical Clustering. J. Mach. Learn. Res. 17 (2016)
Shrinu Kushagra, Samira Samadi, Shai Ben-David
Finding Meaningful Cluster Structure Amidst Background Noise. ALT (2016)
Shai Ben-David
How Far Are We From Having a Satisfactory Theory of Clustering? MFCS (2016)
Hassan Ashtiani, Shrinu Kushagra, Shai Ben-David
Clustering with Same-Cluster Queries. CoRR (2016)
Anastasia Pentina, Shai Ben-David
Multi-task and Lifelong Learning of Kernels. CoRR (2016)
Anastasia Pentina, Shai Ben-David
Multi-task and Lifelong Learning of Kernels. ALT (2015)
Shai Ben-David
Computational Feasibility of Clustering under Clusterability Assumptions. CoRR (2015)
Shai Ben-David
2 Notes on Classes with Vapnik-Chervonenkis Dimension 1. CoRR (2015)
Shrinu Kushagra, Shai Ben-David
Information Preserving Dimensionality Reduction. ALT (2015)
Samory Kpotufe, Ruth Urner, Shai Ben-David
Hierarchical Label Queries with Data-Dependent Partitions. COLT (2015)
Hassan Ashtiani, Shai Ben-David
Representation Learning for Clustering: A Statistical Framework. CoRR (2015)
Amit Daniely, Sivan Sabato, Shai Ben-David, Shai Shalev-Shwartz
Multiclass learnability and the ERM principle. J. Mach. Learn. Res. 16 (2015)
Shai Ben-David
Clustering is Easy When ....What? CoRR (2015)
Hassan Ashtiani, Shai Ben-David
Representation Learning for Clustering: A Statistical Framework. UAI (2015)
Shai Ben-David, Ruth Urner
The sample complexity of agnostic learning under deterministic labels. COLT (2014)
Shai Ben-David, Nika Haghtalab
Clustering in the Presence of Background Noise. ICML (2014)
Shai Ben-David, Ruth Urner
Domain adaptation-can quantity compensate for quality? Ann. Math. Artif. Intell. 70 (3) (2014)
Shai Ben-David, Ruth Urner
The sample complexity of agnostic learning with deterministic labels. ISAIM (2014)
Sharon Wulff, Ruth Urner, Shai Ben-David
Monochromatic Bi-Clustering. ICML (2) (2013)
Ruth Urner, Sharon Wulff, Shai Ben-David
PLAL: Cluster-based active learning. COLT (2013)
Shai Ben-David
A theoretical approach to the clustering selection problem. MultiClust@KDD (2013)
Amit Daniely, Sivan Sabato, Shai Ben-David, Shai Shalev-Shwartz
Multiclass learnability and the ERM principle. CoRR (2013)
Margareta Ackerman, Shai Ben-David, David Loker, Sivan Sabato
Clustering Oligarchies. AISTATS (2013)
Reza Bosagh Zadeh, Shai Ben-David
A Uniqueness Theorem for Clustering CoRR (2012)
Shai Ben-David
Theoretical analysis of domain adaptation - current state of the art. MLSLP (2012)
Shai Ben-David, Ruth Urner
On the Hardness of Domain Adaptation and the Utility of Unlabeled Target Samples. ALT (2012)
Ruth Urner, Shai Ben-David, Ohad Shamir
Learning from Weak Teachers. AISTATS (2012)
Margareta Ackerman, Shai Ben-David, Simina Brânzei, David Loker
Weighted Clustering. AAAI (2012)
Shai Ben-David, David Loker, Nathan Srebro, Karthik Sridharan
Minimizing The Misclassification Error Rate Using a Surrogate Convex Loss. ICML (2012)
Shai Ben-David, Shai Shalev-Shwartz, Ruth Urner
Domain Adaptation--Can Quantity compensate for Quality?. ISAIM (2012)
Amit Daniely, Sivan Sabato, Shai Ben-David, Shai Shalev-Shwartz
Multiclass Learnability and the ERM principle. COLT (2011)
Ruth Urner, Shai Shalev-Shwartz, Shai Ben-David
Access to Unlabeled Data can Speed up Prediction Time. ICML (2011)
Margareta Ackerman, Shai Ben-David
Discerning Linkage-Based Algorithms among Hierarchical Clustering Methods. IJCAI (2011)
Shalev Ben-David, Shai Ben-David
Learning a Classifier when the Labeling Is Known. ALT (2011)
Margareta Ackerman, Shai Ben-David, Simina Brânzei, David Loker
Weighted Clustering CoRR (2011)
Margareta Ackerman, Shai Ben-David, David Loker
Towards Property-Based Classification of Clustering Paradigms. NIPS (2010)
Shai Ben-David, Tyler Lu, Teresa Luu, Dávid Pál
Impossibility Theorems for Domain Adaptation. AISTATS (2010)
Margareta Ackerman, Shai Ben-David, David Loker
Characterization of Linkage-based Clustering. COLT (2010)
Shai Ben-David, John Blitzer, Koby Crammer, Alex Kulesza, Fernando Pereira, Jennifer Wortman Vaughan
A theory of learning from different domains. Mach. Learn. 79 (1-2) (2010)
George Beskales, Mohamed A. Soliman, Ihab F. Ilyas, Shai Ben-David, Yubin Kim
ProbClean: A probabilistic duplicate detection system. ICDE (2010)
Shai Ben-David
Theory-Practice Interplay in Machine Learning - Emerging Theoretical Challenges. ECML/PKDD (1) (2009)
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
RTTS: towards enterprise-level real-time speech transcription and translation services. INTERSPEECH (2009)
Shai Ben-David, Tyler Lu, Dávid Pál, Miroslava Sotáková
Learning Low Density Separators. AISTATS (2009)
Reza Zadeh, Shai Ben-David
A Uniqueness Theorem for Clustering. UAI (2009)
Margareta Ackerman, Shai Ben-David
Clusterability: A Theoretical Study. AISTATS (2009)
Shai Ben-David, Dávid Pál, Shai Shalev-Shwartz
Agnostic Online Learning. COLT (2009)
George Beskales, Mohamed A. Soliman, Ihab F. Ilyas, Shai Ben-David
Modeling and Querying Possible Repairs in Duplicate Detection. Proc. VLDB Endow. 2 (1) (2009)
Shai Ben-David, Tyler Lu, Dávid Pál, Miroslava Sotáková
Learning Low-Density Separators CoRR (2008)
Shai Ben-David, Tyler Lu, Dávid Pál
Does Unlabeled Data Provably Help? Worst-case Analysis of the Sample Complexity of Semi-Supervised Learning. COLT (2008)
Shai Ben-David, Ulrike von Luxburg
Relating Clustering Stability to Properties of Cluster Boundaries. COLT (2008)
Shai Ben-David, Margareta Ackerman
Measures of Clustering Quality: A Working Set of Axioms for Clustering. NIPS (2008)
Shai Ben-David, Reba Schuller Borbely
A notion of task relatedness yielding provable multiple-task learning guarantees. Mach. Learn. 73 (3) (2008)
Shai Ben-David, John Case, Thomas Zeugmann
Foreword. Theor. Comput. Sci. 382 (3) (2007)
Shai Ben-David
-means clustering. Mach. Learn. 66 (2-3) (2007)
Shai Ben-David, Dávid Pál, Hans Ulrich Simon
-Means Clustering. COLT (2007)
Shai Ben-David, John Blitzer, Koby Crammer, Fernando Pereira
Analysis of Representations for Domain Adaptation. NIPS (2006)
Ting He, Shai Ben-David, Lang Tong
Nonparametric change detection and estimation in large-scale sensor networks. IEEE Trans. Signal Process. 54 (4) (2006)
Shai Ben-David
Alternative Measures of Computational Complexity with Applications to Agnostic Learning. TAMC (2006)
Shai Ben-David, Ulrike von Luxburg, Dávid Pál
A Sober Look at Clustering Stability. COLT (2006)
Cristian Budianu, Shai Ben-David, Lang Tong
Estimation of the number of operating sensors in large-scale sensor networks with mobile access. IEEE Trans. Signal Process. 54 (5) (2006)
Nathan Srebro, Shai Ben-David
Learning Bounds for Support Vector Machines with Learned Kernels. COLT (2006)

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Publication Activity (10 Years)

Top Topics

Top Venues

Shai Ben-David