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ORCIDShai Ben-David
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- affiliation: University of Waterloo, School of Computer Science, Canada
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2020 – today
- 2024
[j40]Eli Verwimp, Rahaf Aljundi, 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. Trans. Mach. Learn. Res. 2024 (2024)- [c98]Tosca Lechner, Shai Ben-David:
Inherent limitations of dimensions for characterizing learnability of distribution classes. COLT 2024: 3353-3374 - [i28]Shai Ben-David, Alex Bie, Gautam Kamath, Tosca Lechner:
Distribution Learnability and Robustness. CoRR abs/2406.17814 (2024) - 2023
- [c97]Niki Hasrati, Shai Ben-David:
On Computable Online Learning. ALT 2023: 707-725 - [c96]Tosca Lechner, Ruth Urner, Shai Ben-David:
Strategic Classification with Unknown User Manipulations. ICML 2023: 18714-18732 - [c95]Shai Ben-David, Alex Bie, Gautam Kamath, Tosca Lechner:
Distribution Learnability and Robustness. NeurIPS 2023 - [c94]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 - [i27]Niki Hasrati, Shai Ben-David:
On Computable Online Learning. CoRR abs/2302.04357 (2023) - [i26]Tosca Lechner, Shai Ben-David:
Impossibility of Characterizing Distribution Learning - a simple solution to a long-standing problem. CoRR abs/2304.08712 (2023) - [i25]Shai Ben-David, Alex Bie, Clément L. Canonne, Gautam Kamath
, Vikrant Singhal:
Private Distribution Learning with Public Data: The View from Sample Compression. CoRR abs/2308.06239 (2023) - [i24]Eli Verwimp, Rahaf Aljundi, 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 abs/2311.11908 (2023) - 2022
- [c93]Tosca Lechner, Shai Ben-David:
Inherent Limitations of Multi-Task Fair Representations. CoLLAs 2022: 583-603 - 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
- [b2]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: 858-863 - [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]