Shai Ben-David

Q: What Schools Are Affiliated With Shai Ben-David

Shai Ben-David is affiliated with the following schools: University of Waterloo, University of Haifa, Hebrew University of Jerusalem, Cornell University

Shai Ben-David's AcademicInfluence.com Rankings

Shai Ben-David

Computer Science

#9832

World Rank

#10315

Historical Rank

Machine Learning

#5161

World Rank

#5226

Historical Rank

Artificial Intelligence

#6066

World Rank

#6159

Historical Rank

Database

#9314

World Rank

#9804

Historical Rank

computer-science Degrees

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Computer Science

Why Is Shai Ben-David Influential?

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According to Wikipedia, Shai Ben-David is an Israeli-Canadian computer scientist and professor at the University of Waterloo. He is known for his research in theoretical machine learning. Biography Shai Ben-David grew up in Jerusalem, Israel and received a Ph.D. in mathematics from the Hebrew University of Jerusalem, where he was advised by Saharon Shelah. He held postdoctoral positions in mathematics and computer science at the University of Toronto. He was a professor of computer science at the Technion and also held visiting positions at the Australian National University and Cornell University.

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Shai Ben-David's Published Works

Number of citations in a given year to any of this author's works

Total number of citations to an author for the works they published in a given year. This highlights publication of the most important work(s) by the author

Published Works

Scale-sensitive dimensions, uniform convergence, and learnability (1993) (461)
Stochastic Gradient Descent (2014) (291)
On the theory of average case complexity (1989) (241)
Impossibility Theorems for Domain Adaptation (2010) (236)
On the power of randomization in on-line algorithms (2005) (196)
Measures of Clustering Quality: A Working Set of Axioms for Clustering (2008) (179)
A Uniqueness Theorem for Clustering (2009) (126)
Characterizations of Learnability for Classes of {0, ..., n}-Valued Functions (1995) (122)
A notion of task relatedness yielding provable multiple-task learning guarantees (2008) (116)
Minimizing The Misclassification Error Rate Using a Surrogate Convex Loss (2012) (63)
Hardness results for neural network approximation problems (1999) (61)
Domain adaptation–can quantity compensate for quality? (2014) (60)
Estimation of the number of operating sensors in large-scale sensor networks with mobile access (2006) (59)
Combinatorial Variability of Vapnik-chervonenkis Classes with Applications to Sample Compression Schemes (1998) (51)
A framework for statistical clustering with constant time approximation algorithms for K-median and K-means clustering (2007) (49)
On Shelah’s compactness of cardinals (1978) (43)
The weak □* is really weaker than the full □ (1986) (42)
Learning by distances (1990) (42)
Efficient Learning of Linear Perceptrons (2000) (34)
A modal logic for subjective default reasoning (1994) (33)
A Characterization of Linkage-Based Hierarchical Clustering (2016) (31)
Nonparametric change detection and estimation in large-scale sensor networks (2006) (28)
Can Finite Samples Detect Singularities of Real-Valued Functions? (1992) (27)
Clustering Oligarchies (2013) (26)
A parametrization scheme for classifying models of learnability (1989) (25)
Near-optimal Sample Complexity Bounds for Robust Learning of Gaussian Mixtures via Compression Schemes (2017) (25)
Domain Adaptation as Learning with Auxiliary Information (2013) (15)
Enforcing Interpretability and its Statistical Impacts: Trade-offs between Accuracy and Interpretability (2020) (14)
Souslin trees and successors of singular cardinals (1986) (11)
Monochromatic Bi-Clustering (2013) (10)
Scale-sensitive Dimensions, Uniform Convergence, (1993) (8)
Non-special Aronszajn trees on ℵω+1 (1986) (7)
The two-cardinals transfer property and resurrection of supercompactness (1996) (7)
On Learnability wih Computable Learners (2020) (6)
Learning a Classifier when the Labeling Is Known (2011) (6)
An Efficient Method to Impose Fairness in Linear Models (2017) (6)
Settling the Sample Complexity for Learning Mixtures of Gaussians (2017) (6)
2 Notes on Classes with Vapnik-Chervonenkis Dimension 1 (2015) (6)
On Learnability with Computable Learners (2020) (5)
Eecient Learning of Linear Perceptrons (2000) (4)
Open Problem: Are all VC-classes CPAC learnable? (2021) (3)
Provably noise-robust, regularised k-means clustering (2017) (3)
A Note on VC-Dimension and Measure of Sets of Reals (2000) (3)
Algorithmic Learning Theory (2004) (2)
On ultrafilters and NP (1994) (2)
On shelah’s compactness of cardinals (1978) (1)
Understanding Machine Learning: Generative Models (2014) (1)
On Computable Online Learning (2023) (1)
A note on VC-dimension and measures of sets of reals (1995) (1)
On Learning in the Limit and Non-Uniform (epsilon, delta)-Learning. (1993) (1)
Classification Using Information (1994) (1)
The Runtime of Learning (2014) (1)
Dispositif de notification sonore (2009) (0)
Proof of the Fundamental Theorem of Learning Theory (2014) (0)
Impossibility of Characterizing Distribution Learning - a simple solution to a long-standing problem (2023) (0)
Domain adaptation–can quantity compensate for quality? (2013) (0)
Regularization and Stability (2014) (0)
תכונות קומבינטוריות של מונים עוקבים לסינגולריים (Combinatorial properties of successors of singular Cardinals.) (1986) (0)
Understanding Machine Learning: The VC-Dimension (2014) (0)
Understanding Machine Learning: Convex Learning Problems (2014) (0)
PROBLEMS IN COMPUTERIZED LEARNABILITY (2014) (0)
Machine Learning : Foundations and Algorithms (2012) (0)
Author Correction: Learnability can be undecidable (2019) (0)
The Global Time ~sumption and Semantics For· Concurrent Systems (extended Adstract) (2013) (0)
A NOTE ON VC-DIMENSION AND MEASURE (1994) (0)
4 Secret Sharing over the Reals (2007) (0)
Understanding Machine Learning: Measure Concentration (2014) (0)
Classiication Using Information (1994) (0)
Classification Confidence Scores with Point-wise Guarantees (2021) (0)
Theoretical analysis of domain adaptation - current state of the art (2012) (0)
Learnability can be undecidable (2019) (0)
ON THE INDEPENDENCE OF P VERSUS NP (REVISED VERSION). (1992) (0)
M L ] 1 8 A ug 2 01 6 Multitask and Lifelong Learning of Kernels (2018) (0)
The Informational Hardness of Covariate Shift Learning and the utility of unlabeled target samples (2012) (0)
98 16382 – Foundations of Unsupervised Learning 3 Overview of Talks 3 . 1 Linear Algebraic Structure of Word Meanings (2017) (0)
L G ] 1 0 O ct 2 01 8 Semi-supervised clustering for deduplication (2018) (0)
Supplementary : Semi-supervised clustering for deduplication (2019) (0)
Identifying Regions of Trusted Predictions Supplementary Material (2021) (0)
Learning via Uniform Convergence (2014) (0)
Foreword (2007) (0)

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Other Resources About Shai Ben-David

en.wikipedia.org

What Schools Are Affiliated With Shai Ben-David?

Shai Ben-David is affiliated with the following schools:

Shai Ben-David's Academic­Influence.com Rankings

Why Is Shai Ben-David Influential?

Shai Ben-David's Published Works

Published Works

Other Resources About Shai Ben-David

What Schools Are Affiliated With Shai Ben-David?

Shai Ben-David's AcademicInfluence.com Rankings