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D-Index & Metrics

Computer Science

D-Index
56
Citations
20100
World Ranking
3965
National Ranking
151

Overview

Shai Ben-David is affiliated with the University of Waterloo in Canada and specializes in the field of Computer Science. Their research covers a range of subfields including Artificial Intelligence, Computational Theory and Mathematics, Signal Processing, Control and Systems Engineering, and Statistical and Nonlinear Physics.

Their work primarily focuses on topics such as Machine Learning and Algorithms, Advanced Clustering Algorithms Research, Data Management and Algorithms, Machine Learning and Data Classification, Adversarial Robustness in Machine Learning, Privacy-Preserving Technologies in Data, and Rough Sets and Fuzzy Logic.

Ben-David has published extensively in venues such as:

  • arXiv (Cornell University)
  • Proceedings of the AAAI Conference on Artificial Intelligence
  • Pattern Recognition
  • Journal of the ACM
  • Proceedings of the AAAI/ACM Conference on AI Ethics and Society

Their recent papers include:

  • "Weighted Clustering" (2021), Proceedings of the AAAI Conference on Artificial Intelligence
  • "Enforcing Interpretability and its Statistical Impacts: Trade-offs between Accuracy and Interpretability" (2020), arXiv (Cornell University)
  • "Weighted clustering: Towards solving the user's dilemma" (2021), Pattern Recognition
  • "Near-optimal Sample Complexity Bounds for Robust Learning of Gaussian Mixtures via Compression Schemes" (2020), Journal of the ACM
  • "Continual Learning: Applications and the Road Forward" (2023), arXiv (Cornell University)

Frequent collaborators include Tosca Lechner, Margareta Ackerman, Simina Brânzei, David Loker, and Alex Bie. Ben-David has coauthored multiple works with these researchers, with the highest number of collaborations recorded with Tosca Lechner.

Best Publications

  • Understanding Machine Learning: From Theory To Algorithms

    Shai Shalev-Shwartz;Shai Ben-David

  • A theory of learning from different domains

    Shai Ben-David;John Blitzer;Koby Crammer;Alex Kulesza

  • Analysis of Representations for Domain Adaptation

    Shai Ben-David;John Blitzer;Koby Crammer;Fernando Pereira

  • Detecting change in data streams

    Daniel Kifer;Shai Ben-David;Johannes Gehrke

  • On the power of randomization in on-line algorithms

    S. Ben-David;A. Borodin;R. Karp;G. Tardos

  • Scale-sensitive dimensions, uniform convergence, and learnability

    Noga Alon;Shai Ben-David;Nicolò Cesa-Bianchi;David Haussler

  • Exploiting Task Relatedness for Multiple Task Learning

    Shai Ben-David;Shai Ben-David;Reba Schuller

  • On the theory of average case complexity

    Shai Ben-David;Benny Chor;Oded Goldreich;Michael Luby

  • A sober look at clustering stability

    Shai Ben-David;Ulrike von Luxburg;Dávid Pál

  • On the power of randomization in online algorithms

    S. Ben-David;A. Borodin;R. Karp;G. Tardos

  • Impossibility Theorems for Domain Adaptation

    Shai Ben-David;Tyler Lu;Teresa Luu;Dávid Pál

  • Measures of Clustering Quality: A Working Set of Axioms for Clustering

    Shai Ben-David;Margareta Ackerman

  • Empirical Risk Minimization Under Fairness Constraints

    Michele Donini;Luca Oneto;Shai Ben-David;John S. Shawe-Taylor

  • On the difficulty of approximately maximizing agreements

    Shai Ben-David;Nadav Eiron;Philip M. Long

  • A new measure for the study of on-line algorithms

    Shai Ben-David;Allan Borodin

  • Clusterability: A Theoretical Study

    Margareta Ackerman;Shai Ben-David

  • Characterizations of Learnability for Classes of {0, ..., n)-Valued Functions

    S. Bendavid;N. Cesabianchi;D. Haussler;P.M. Long

  • A uniqueness theorem for clustering

    Reza Bosagh Zadeh;Shai Ben-David

  • Does Unlabeled Data Provably Help? Worst-case Analysis of the Sample Complexity of Semi-Supervised Learning.

    Shai Ben-David;Tyler Lu;Dávid Pál

  • Agnostic Online Learning.

    Shai Ben-David;Dávid Pál;Shai Shalev-Shwartz

  • On the Power of Randomization in Online Algorithms (Extended Abstract)

    Shai Ben-David;Allan Borodin;Richard M. Karp;Gábor Tardos

Frequent Co-Authors

Shai Shalev-Shwartz
Shai Shalev-Shwartz Hebrew University of Jerusalem
Eyal Kushilevitz
Eyal Kushilevitz Technion – Israel Institute of Technology
Michael Lindenbaum
Michael Lindenbaum Technion – Israel Institute of Technology
Yishay Mansour
Yishay Mansour Tel Aviv University
Ihab F. Ilyas
Ihab F. Ilyas University of Waterloo
Oded Goldreich
Oded Goldreich Weizmann Institute of Science
Benny Chor
Benny Chor Tel Aviv University
Peter L. Bartlett
Peter L. Bartlett University of California, Berkeley
Nicolò Cesa-Bianchi
Nicolò Cesa-Bianchi University of Milan
Philip M. Long
Philip M. Long Google (United States)

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