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ORCIDShai Ben-David
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- affiliation: School of Computer Science, University of Waterloo
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2020 – today
- 2021
[j39]Margareta Ackerman, Shai Ben-David, Simina Brânzei, David Loker:
Weighted clustering: Towards solving the user's dilemma. Pattern Recognit. 120: 108152 (2021)- [c92]Nivasini Ananthakrishnan, Shai Ben-David, Tosca Lechner:
Classification Confidence Scores with Point-wise Guarantees. SafeAI@AAAI 2021 - [c91]Sushant Agarwal, Nivasini Ananthakrishnan, Shai Ben-David, Tosca Lechner, Ruth Urner:
Open Problem: Are all VC-classes CPAC learnable? COLT 2021: 4636-4641 - [c90]Shai Ben-David, Pavel Hrubes, Shay Moran
, Amir Shpilka, Amir Yehudayoff:
Learnability can be independent of set theory (invited paper). STOC 2021: 11 - [c89]Nivasini Ananthakrishnan, Shai Ben-David, Tosca Lechner, Ruth Urner:
Identifying regions of trusted predictions. UAI 2021: 2125-2134 - [i23]Tosca Lechner, Shai Ben-David, Sushant Agarwal, Nivasini Ananthakrishnan:
Impossibility results for fair representations. CoRR abs/2107.03483 (2021) - 2020
- [j38]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): 32:1-32:42 (2020) - [c88]Sushant Agarwal, Nivasini Ananthakrishnan, Shai Ben-David, Tosca Lechner, Ruth Urner:
On Learnability wih Computable Learners. ALT 2020: 48-60 - [i22]Gintare Karolina Dziugaite, Shai Ben-David, Daniel M. Roy:
Enforcing Interpretability and its Statistical Impacts: Trade-offs between Accuracy and Interpretability. CoRR abs/2010.13764 (2020)
2010 – 2019
- 2019
- [j37]Shai Ben-David, Pavel Hrubes, Shay Moran, Amir Shpilka, Amir Yehudayoff:
Learnability can be undecidable. Nat. Mach. Intell. 1(1): 44-48 (2019) - [j36]Shai Ben-David, Pavel Hrubes, Shay Moran, Amir Shpilka, Amir Yehudayoff:
Author Correction: Learnability can be undecidable. Nat. Mach. Intell. 1(2): 121 (2019) - [c87]Shrinu Kushagra, Shai Ben-David, Ihab F. Ilyas:
Semi-supervised clustering for de-duplication. AISTATS 2019: 1659-1667 - [c86]Christina Göpfert, Shai Ben-David, Olivier Bousquet, Sylvain Gelly, Ilya O. Tolstikhin, Ruth Urner:
When can unlabeled data improve the learning rate? COLT 2019: 1500-1518 - [c85]Shrinu Kushagra, Hemant Saxena, Ihab F. Ilyas, Shai Ben-David:
A Semi-Supervised Framework of Clustering Selection for De-Duplication. ICDE 2019: 208-219 - [i21]Christina Göpfert, Shai Ben-David, Olivier Bousquet, Sylvain Gelly, Ilya O. Tolstikhin, Ruth Urner:
When can unlabeled data improve the learning rate? CoRR abs/1905.11866 (2019) - 2018
- [c84]Hassan Ashtiani, Shai Ben-David, Abbas Mehrabian:
Sample-Efficient Learning of Mixtures. AAAI 2018: 2679-2686 - [c83]Shai Ben-David:
Clustering - What Both Theoreticians and Practitioners Are Doing Wrong. AAAI 2018: 7962-7964 - [c82]Anastasia Pentina, Shai Ben-David:
Multi-task {K}ernel {L}earning Based on {P}robabilistic {L}ipschitzness. ALT 2018: 682-701 - [c81]Michele Donini, Luca Oneto, Shai Ben-David, John Shawe-Taylor, Massimiliano Pontil:
Empirical Risk Minimization Under Fairness Constraints. NeurIPS 2018: 2796-2806 - [c80]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: 3416-3425 - [i20]Michele Donini, Luca Oneto, Shai Ben-David, John Shawe-Taylor, Massimiliano Pontil:
Empirical Risk Minimization under Fairness Constraints. CoRR abs/1802.08626 (2018) - [i19]Shai Ben-David:
Clustering - What Both Theoreticians and Practitioners are Doing Wrong. CoRR abs/1805.08838 (2018) - [i18]Shrinu Kushagra, Shai Ben-David, Ihab F. Ilyas:
Semi-supervised clustering for de-duplication. CoRR abs/1810.04361 (2018) - 2017
- [i17]Hassan Ashtiani, Shai Ben-David, Abbas Mehrabian:
Sample-Efficient Learning of Mixtures. CoRR abs/1706.01596 (2017) - [i16]Hassan Ashtiani, Shai Ben-David, Abbas Mehrabian:
Agnostic Distribution Learning via Compression. CoRR abs/1710.05209 (2017) - [i15]Shai Ben-David, Pavel Hrubes, Shay Moran, Amir Shpilka, Amir Yehudayoff:
A learning problem that is independent of the set theory ZFC axioms. CoRR abs/1711.05195 (2017) - [i14]Shrinu Kushagra, Nicole McNabb, Yaoliang Yu, Shai Ben-David:
Provably noise-robust, regularised k-means clustering. CoRR abs/1711.11247 (2017) - 2016
- [j35]Margareta Ackerman, Shai Ben-David:
A Characterization of Linkage-Based Hierarchical Clustering. J. Mach. Learn. Res. 17: 232:1-232:17 (2016) - [c79]Shai Ben-David, Ruth Urner:
On Version Space Compression. ALT 2016: 50-64 - [c78]Shrinu Kushagra, Samira Samadi, Shai Ben-David:
Finding Meaningful Cluster Structure Amidst Background Noise. ALT 2016: 339-354 - [c77]Shai Ben-David:
How Far Are We From Having a Satisfactory Theory of Clustering? MFCS 2016: 1:1-1:1 - [c76]Hassan Ashtiani, Shrinu Kushagra, Shai Ben-David:
Clustering with Same-Cluster Queries. NIPS 2016: 3216-3224 - [i13]Anastasia Pentina, Shai Ben-David:
Multi-task and Lifelong Learning of Kernels. CoRR abs/1602.06531 (2016) - [i12]Hassan Ashtiani, Shrinu Kushagra, Shai Ben-David:
Clustering with Same-Cluster Queries. CoRR abs/1606.02404 (2016) - [i11]Maria-Florina Balcan, Shai Ben-David, Ruth Urner, Ulrike von Luxburg:
Foundations of Unsupervised Learning (Dagstuhl Seminar 16382). Dagstuhl Reports 6(9): 94-109 (2016) - 2015
- [j34]Amit Daniely, Sivan Sabato, Shai Ben-David, Shai Shalev-Shwartz:
Multiclass learnability and the ERM principle. J. Mach. Learn. Res. 16: 2377-2404 (2015) - [c75]Anastasia Pentina, Shai Ben-David:
Multi-task and Lifelong Learning of Kernels. ALT 2015: 194-208 - [c74]Shrinu Kushagra, Shai Ben-David:
Information Preserving Dimensionality Reduction. ALT 2015: 239-253 - [c73]Samory Kpotufe, Ruth Urner, Shai Ben-David:
Hierarchical Label Queries with Data-Dependent Partitions. COLT 2015: 1176-1189 - [c72]Hassan Ashtiani, Shai Ben-David:
Representation Learning for Clustering: A Statistical Framework. UAI 2015: 82-91 - [i10]Shai Ben-David:
Computational Feasibility of Clustering under Clusterability Assumptions. CoRR abs/1501.00437 (2015) - [i9]Hassan Ashtiani, Shai Ben-David:
Representation Learning for Clustering: A Statistical Framework. CoRR abs/1506.05900 (2015) - [i8]Shai Ben-David:
2 Notes on Classes with Vapnik-Chervonenkis Dimension 1. CoRR abs/1507.05307 (2015) - [i7]Shai Ben-David:
Clustering is Easy When ....What? CoRR abs/1510.05336 (2015) - 2014
- [b1]Shai Shalev-Shwartz, Shai Ben-David:
Understanding Machine Learning - From Theory to Algorithms. Cambridge University Press 2014, ISBN 978-1-10-705713-5, pp. I-XVI, 1-397 - [j33]Shai Ben-David, Ruth Urner:
Domain adaptation-can quantity compensate for quality? Ann. Math. Artif. Intell. 70(3): 185-202 (2014) - [c71]Shai Ben-David, Ruth Urner:
The sample complexity of agnostic learning under deterministic labels. COLT 2014: 527-542 - [c70]Shai Ben-David, Nika Haghtalab:
Clustering in the Presence of Background Noise. ICML 2014: 280-288 - [c69]Shai Ben-David, Ruth Urner:
The sample complexity of agnostic learning with deterministic labels. ISAIM 2014 - 2013
- [c68]Margareta Ackerman, Shai Ben-David, David Loker, Sivan Sabato:
Clustering Oligarchies. AISTATS 2013: 66-74 - [c67]Ruth Urner, Sharon Wulff, Shai Ben-David:
PLAL: Cluster-based active learning. COLT 2013: 376-397 - [c66]Sharon Wulff, Ruth Urner, Shai Ben-David:
Monochromatic Bi-Clustering. ICML (2) 2013: 145-153 - [c65]Shai Ben-David:
A theoretical approach to the clustering selection problem. MultiClust@KDD 2013: 1 - [i6]Amit Daniely, Sivan Sabato, Shai Ben-David, Shai Shalev-Shwartz:
Multiclass learnability and the ERM principle. CoRR abs/1308.2893 (2013) - 2012
- [c64]Margareta Ackerman, Shai Ben-David, Simina Brânzei, David Loker:
Weighted Clustering. AAAI 2012 - [c63]Shai Ben-David, Ruth Urner:
On the Hardness of Domain Adaptation and the Utility of Unlabeled Target Samples. ALT 2012: 139-153 - [c62]Shai Ben-David, David Loker, Nathan Srebro, Karthik Sridharan:
Minimizing The Misclassification Error Rate Using a Surrogate Convex Loss. ICML 2012 - [c61]Shai Ben-David, Shai Shalev-Shwartz, Ruth Urner:
Domain Adaptation--Can Quantity compensate for Quality?. ISAIM 2012 - [c60]Shai Ben-David:
Theoretical analysis of domain adaptation - current state of the art. MLSLP 2012 - [c59]Ruth Urner, Shai Ben-David, Ohad Shamir:
Learning from Weak Teachers. AISTATS 2012: 1252-1260 - [i5]Reza Bosagh Zadeh, Shai Ben-David:
A Uniqueness Theorem for Clustering. CoRR abs/1205.2600 (2012) - 2011
- [c58]Shalev Ben-David, Shai Ben-David:
Learning a Classifier when the Labeling Is Known. ALT 2011: 440-451 - [c57]Ruth Urner, Shai Shalev-Shwartz, Shai Ben-David:
Access to Unlabeled Data can Speed up Prediction Time. ICML 2011: 641-648 - [c56]Margareta Ackerman, Shai Ben-David:
Discerning Linkage-Based Algorithms among Hierarchical Clustering Methods. IJCAI 2011: 1140-1145 - [c55]Amit Daniely, Sivan Sabato, Shai Ben-David, Shai Shalev-Shwartz:
Multiclass Learnability and the ERM principle. COLT 2011: 207-232 - [i4]Margareta Ackerman, Shai Ben-David, Simina Brânzei, David Loker:
Weighted Clustering. CoRR abs/1109.1844 (2011) - 2010
- [j32]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): 151-175 (2010) - [c54]Margareta Ackerman, Shai Ben-David, David Loker:
Characterization of Linkage-based Clustering. COLT 2010: 270-281 - [c53]George Beskales, Mohamed A. Soliman, Ihab F. Ilyas, Shai Ben-David, Yubin Kim:
ProbClean: A probabilistic duplicate detection system. ICDE 2010: 1193-1196 - [c52]Margareta Ackerman, Shai Ben-David, David Loker:
Towards Property-Based Classification of Clustering Paradigms. NIPS 2010: 10-18 - [c51]Shai Ben-David, Tyler Lu, Teresa Luu, Dávid Pál:
Impossibility Theorems for Domain Adaptation. AISTATS 2010: 129-136
2000 – 2009
- 2009
- [j31]George Beskales, Mohamed A. Soliman, Ihab F. Ilyas, Shai Ben-David:
Modeling and Querying Possible Repairs in Duplicate Detection. Proc. VLDB Endow. 2(1): 598-609 (2009) - [c50]Shai Ben-David, Dávid Pál, Shai Shalev-Shwartz:
Agnostic Online Learning. COLT 2009 - [c49]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: 436-439 - [c48]Shai Ben-David:
Theory-Practice Interplay in Machine Learning - Emerging Theoretical Challenges. ECML/PKDD (1) 2009: 1 - [c47]Reza Zadeh, Shai Ben-David:
A Uniqueness Theorem for Clustering. UAI 2009: 639-646 - [c46]Margareta Ackerman, Shai Ben-David:
Clusterability: A Theoretical Study. AISTATS 2009: 1-8 - [c45]Shai Ben-David, Tyler Lu, Dávid Pál, Miroslava Sotáková:
Learning Low Density Separators. AISTATS 2009: 25-32 - 2008
- [j30]Shai Ben-David, Reba Schuller Borbely:
A notion of task relatedness yielding provable multiple-task learning guarantees. Mach. Learn. 73(3): 273-287 (2008) - [c44]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: 33-44 - [c43]Shai Ben-David, Ulrike von Luxburg:
Relating Clustering Stability to Properties of Cluster Boundaries. COLT 2008: 379-390 - [c42]Shai Ben-David, Margareta Ackerman:
Measures of Clustering Quality: A Working Set of Axioms for Clustering. NIPS 2008: 121-128 - [i3]Shai Ben-David, Tyler Lu, Dávid Pál, Miroslava Sotáková:
Learning Low-Density Separators. CoRR abs/0805.2891 (2008) - 2007
- [j29]Shai Ben-David:
A framework for statistical clustering with constant time approximation algorithms for K-median and K-means clustering. Mach. Learn. 66(2-3): 243-257 (2007) - [j28]Shai Ben-David, John Case, Thomas Zeugmann:
Foreword. Theor. Comput. Sci. 382(3): 167-169 (2007) - [c41]Shai Ben-David, Dávid Pál, Hans Ulrich Simon:
Stability of k -Means Clustering. COLT 2007: 20-34 - 2006
- [j27]Ting He, Shai Ben-David, Lang Tong:
Nonparametric change detection and estimation in large-scale sensor networks. IEEE Trans. Signal Process. 54(4): 1204-1217 (2006) - [j26]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): 1703-1715 (2006) - [c40]Shai Ben-David, Ulrike von Luxburg, Dávid Pál:
A Sober Look at Clustering Stability. COLT 2006: 5-19 - [c39]Nathan Srebro, Shai Ben-David:
Learning Bounds for Support Vector Machines with Learned Kernels. COLT 2006: 169-183 - [c38]Shai Ben-David, John Blitzer, Koby Crammer, Fernando Pereira:
Analysis of Representations for Domain Adaptation. NIPS 2006: 137-144 - [c37]Shai Ben-David:
Alternative Measures of Computational Complexity with Applications to Agnostic Learning. TAMC 2006: 231-235 - 2005
- [c36]Ting He, Shai Ben-David, Lang Tong:
Nonparametric change detection in 2D random sensor field. ICASSP (4) 2005: 821-824 - 2004
- [c35]Shai Ben-David:
A Framework for Statistical Clustering with a Constant Time Approximation Algorithms for K-Median Clustering. COLT 2004: 415-426 - [c34]Daniel Kifer, Shai Ben-David, Johannes Gehrke:
Detecting Change in Data Streams. VLDB 2004: 180-191 - [e3]Shai Ben-David, John Case, Akira Maruoka:
Algorithmic Learning Theory, 15th International Conference, ALT 2004, Padova, Italy, October 2-5, 2004, Proceedings. Lecture Notes in Computer Science 3244, Springer 2004, ISBN 978-3-540-23356-5 [contents] - 2003
- [j25]Shai Ben-David, Nadav Eiron, Philip M. Long:
On the difficulty of approximately maximizing agreements. J. Comput. Syst. Sci. 66(3): 496-514 (2003) - [c33]Shai Ben-David, Reba Schuller:
Exploiting Task Relatedness for Mulitple Task Learning. COLT 2003: 567-580 - 2002
- [j24]Shai Ben-David, Nadav Eiron, Hans Ulrich Simon:
The Computational Complexity of Densest Region Detection. J. Comput. Syst. Sci. 64(1): 22-47 (2002) - [j23]Shai Ben-David, Nadav Eiron, Hans Ulrich Simon:
Limitations of Learning Via Embeddings in Euclidean Half Spaces. J. Mach. Learn. Res. 3: 441-461 (2002) - [j22]Peter L. Bartlett, Shai Ben-David:
Hardness results for neural network approximation problems. Theor. Comput. Sci. 284(1): 53-66 (2002) - [c32]Shai Ben-David, Johannes Gehrke, Reba Schuller:
A theoretical framework for learning from a pool of disparate data sources. KDD 2002: 443-449 - 2001
- [c31]Shai Ben-David, Nadav Eiron, Hans Ulrich Simon:
Limitations of Learning via Embeddings in Euclidean Half-Spaces. COLT/EuroCOLT 2001: 385-401 - [c30]Shai Ben-David, Philip M. Long, Yishay Mansour:
Agnostic Boosting. COLT/EuroCOLT 2001: 507-516 - 2000
- [j21]Shai Ben-David, Rachel Ben-Eliyahu-Zohary:
A modal logic for subjective default reasoning. Artif. Intell. 116(1-2): 217-236 (2000) - [j20]Shai Ben-David, Leonid Gurvits:
A Note On Vc-Dimension And Measure Of Sets Of Reals. Comb. Probab. Comput. 9(5): 391-405 (2000) - [j19]Shai Ben-David, Klaus Meer, Christian Michaux:
A Note on Non-complete Problems in NPImage. J. Complex. 16(1): 324-332 (2000) - [j18]Peter L. Bartlett, Shai Ben-David, Sanjeev R. Kulkarni:
Learning Changing Concepts by Exploiting the Structure of Change. Mach. Learn. 41(2): 153-174 (2000) - [c29]Ron Meir, Ran El-Yaniv, Shai Ben-David:
Localized Boosting. COLT 2000: 190-199 - [c28]Shai Ben-David, Nadav Eiron, Hans Ulrich Simon:
The Computational Complexity of Densest Region Detection. COLT 2000: 255-265 - [c27]Shai Ben-David, Nadav Eiron, Philip M. Long:
On the Difficulty of Approximately Maximizing Agreements. COLT 2000: 266-274 - [c26]Shai Ben-David, Hans Ulrich Simon:
Efficient Learning of Linear Perceptrons. NIPS 2000: 189-195
1990 – 1999
- 1999
- [j17]Michael Lindenbaum, Shai Ben-David:
VC-Dimension Analysis of Object Recognition Tasks. J. Math. Imaging Vis. 10(1): 27-49 (1999) - [c25]Peter L. Bartlett, Shai Ben-David:
Hardness Results for Neural Network Approximation Problems. EuroCOLT 1999: 50-62 - [e2]Shai Ben-David, Philip M. Long:
Proceedings of the Twelfth Annual Conference on Computational Learning Theory, COLT 1999, Santa Cruz, CA, USA, July 7-9, 1999. ACM 1999, ISBN 1-58113-167-4 [contents] - 1998
- [j16]Shai Ben-David:
Can Finite Samples Detect Singularities of Reao-Valued Functions? Algorithmica 22(1/2): 3-17 (1998) - [j15]Shai Ben-David, Ami Litman:
Combinatorial Variability of Vapnik-chervonenkis Classes with Applications to Sample Compression Schemes. Discret. Appl. Math. 86(1): 3-25 (1998) - [j14]Shai Ben-David, Eli Dichterman:
Learning with Restricted Focus of Attention. J. Comput. Syst. Sci. 56(3): 277-298 (1998) - [j13]Shai Ben-David, Michael Lindenbaum:
Localization vs. Identification of Semi-Algebraic Sets. Mach. Learn. 32(3): 207-224 (1998) - [j12]Shai Ben-David, Nadav Eiron:
Self-Directed Learning and Its Relation to the VC-Dimension and to Teacher-Directed Learning. Mach. Learn. 33(1): 87-104 (1998) - [i2]Shai Ben-David, Anna Gringauze:
On the Existence of Propositional Proof Systems and Oracle-relativized Propositional Logic. Electron. Colloquium Comput. Complex. 5(21) (1998) - 1997
- [j11]Noga Alon, Shai Ben-David, Nicolò Cesa-Bianchi, David Haussler:
Scale-sensitive dimensions, uniform convergence, and learnability. J. ACM 44(4): 615-631 (1997) - [j10]Shai Ben-David, Michael Lindenbaum:
Learning Distributions by Their Density Levels: A Paradigm for Learning without a Teacher. J. Comput. Syst. Sci. 55(1): 171-182 (1997) - [j9]Shai Ben-David, Eyal Kushilevitz, Yishay Mansour:
Online Learning versus Offline Learning. Mach. Learn. 29(1): 45-63 (1997) - [c24]Shai Ben-David, Nader H. Bshouty, Eyal Kushilevitz:
A Composition Theorem for Learning Algorithms with Applications to Geometric Concept Classes. STOC 1997: 324-333 - [e1]Shai Ben-David:
Computational Learning Theory, Third European Conference, EuroCOLT '97, Jerusalem, Israel, March 17-19, 1997, Proceedings. Lecture Notes in Computer Science 1208, Springer 1997, ISBN 3-540-62685-9 [contents] - 1996
- [c23]Peter L. Bartlett, Shai Ben-David, Sanjeev R. Kulkarni:
Learning Changing Concepts by Exploiting the Structure of Change. COLT 1996: 131-139 - [i1]Shai Ben-David, Nader H. Bshouty, Eyal Kushilevitz:
A Composition Theorem for Learning Algorithms with Applications to Geometric Concept Classes. Electron. Colloquium Comput. Complex. 3(59) (1996) - 1995
- [j8]Shai Ben-David, Alon Itai, Eyal Kushilevitz:
Learning by Distances. Inf. Comput. 117(2): 240-250 (1995) - [j7]Shai Ben-David, Gyora M. Benedek, Yishay Mansour:
A Parametrization Scheme for Classifying Models of PAC Learnability. Inf. Comput. 120(1): 11-21 (1995) - [j6]Shai Ben-David, Nicolò Cesa-Bianchi, David Haussler, Philip M. Long:
Characterizations of Learnability for Classes of {0, ..., n}-Valued Functions. J. Comput. Syst. Sci. 50(1): 74-86 (1995) - [c22]Shai Ben-David, Nadav Eiron, Eyal Kushilevitz:
On Self-Directed Learning. COLT 1995: 136-143 - [c21]