Aryeh Kontorovich

ORCID

Publication Activity (10 Years)

Years Active: 2011-2024
Publications (10 Years): 82

Top Venues

CoRR

IEEE Trans. Inf. Theory

ALT

COLT

Publications

Aryeh Kontorovich, Amichai Painsky
Distribution Estimation under the Infinity Norm. CoRR (2024)
Steve Hanneke, Aryeh Kontorovich, Guy Kornowski
Efficient Agnostic Learning with Average Smoothness. ALT (2024)
Hananel Zaichyk, Armin Biess, Aryeh Kontorovich, Yury Makarychev
Efficient Kirszbraun extension with applications to regression. Math. Program. 207 (1) (2024)
Aryeh Kontorovich
Aggregation of expert advice, revisited. CoRR (2024)
Moïse Blanchard, Doron Cohen, Aryeh Kontorovich
Correlated Binomial Process. COLT (2024)
Hananel Zaichyk, Armin Biess, Aryeh Kontorovich, Yury Makarychev
Correction: Efficient Kirszbraun extension with applications to regression. Math. Program. 207 (1) (2024)
Matan Levi, Aryeh Kontorovich
Splitting the Difference on Adversarial Training. USENIX Security Symposium (2024)
László Györfi, Aryeh Kontorovich, Roi Weiss
Tree Density Estimation. IEEE Trans. Inf. Theory 69 (2) (2023)
Steve Hanneke, Aryeh Kontorovich, Guy Kornowski
Efficient Agnostic Learning with Average Smoothness. CoRR (2023)
Doron Cohen, Aryeh Kontorovich
Local Glivenko-Cantelli. COLT (2023)
Doron Cohen, Aryeh Kontorovich
Open problem: log(n) factor in "Local Glivenko-Cantelli. COLT (2023)
Steve Hanneke, Aryeh Kontorovich, Guy Kornowski
Near-optimal learning with average Hölder smoothness. CoRR (2023)
Doron Cohen, Aryeh Kontorovich, Aaron Koolyk, Geoffrey Wolfer
Dimension-Free Empirical Entropy Estimation. IEEE Trans. Inf. Theory 69 (5) (2023)
Matan Levi, Aryeh Kontorovich
Splitting the Difference on Adversarial Training. CoRR (2023)
Guy Kornowski, Steve Hanneke, Aryeh Kontorovich
Near-optimal learning with average Hölder smoothness. NeurIPS (2023)
Aryeh Kontorovich, Menachem Sadigurschi, Uri Stemmer
Adaptive Data Analysis with Correlated Observations. ICML (2022)
Lee-Ad Gottlieb, Aryeh Kontorovich
Non-uniform packings. Inf. Process. Lett. 174 (2022)
Aryeh Kontorovich, Menachem Sadigurschi, Uri Stemmer
Adaptive Data Analysis with Correlated Observations. CoRR (2022)
Olivier Bousquet, Haim Kaplan, Aryeh Kontorovich, Yishay Mansour, Shay Moran, Menachem Sadigurschi, Uri Stemmer
Differentially-Private Bayes Consistency. CoRR (2022)
Idan Attias, Aryeh Kontorovich, Yishay Mansour
Improved Generalization Bounds for Adversarially Robust Learning. J. Mach. Learn. Res. 23 (2022)
Lee-Ad Gottlieb, Eran Kaufman, Aryeh Kontorovich, Gabriel Nivasch
Learning Convex Polyhedra With Margin. IEEE Trans. Inf. Theory 68 (3) (2022)
Matan Levi, Idan Attias, Aryeh Kontorovich
Domain Invariant Adversarial Learning. Trans. Mach. Learn. Res. 2022 (2022)
Dan Tsir Cohen, Aryeh Kontorovich
Metric-valued regression. CoRR (2022)
Dan Tsir Cohen, Aryeh Kontorovich
Learning with metric losses. COLT (2022)
Doron Cohen, Aryeh Kontorovich, Aaron Koolyk, Geoffrey Wolfer
Dimension-Free Empirical Entropy Estimation. CoRR (2021)
Lee-Ad Gottlieb, Eran Kaufman, Aryeh Kontorovich, Gabriel Nivasch, Ofir Pele
Nested Barycentric Coordinate System as an Explicit Feature Map. AISTATS (2021)
Matan Levi, Idan Attias, Aryeh Kontorovich
Domain Invariant Adversarial Learning. CoRR (2021)
Lee-Ad Gottlieb, Eran Kaufman, Aryeh Kontorovich
Apportioned margin approach for cost sensitive large margin classifiers. Ann. Math. Artif. Intell. 89 (12) (2021)
Yair Ashlagi, Lee-Ad Gottlieb, Aryeh Kontorovich
Functions with average smoothness: structure, algorithms, and learning. COLT (2021)
Aryeh Kontorovich, Idan Attias
Fat-shattering dimension of k-fold maxima. CoRR (2021)
László Györfi, Aryeh Kontorovich, Roi Weiss
Tree density estimation. CoRR (2021)
Doron Cohen, Aryeh Kontorovich, Aaron Koolyk, Geoffrey Wolfer
Dimension-free empirical entropy estimation. NeurIPS (2021)
Steve Hanneke, Aryeh Kontorovich
Stable Sample Compression Schemes: New Applications and an Optimal SVM Margin Bound. ALT (2021)
Steve Hanneke, Aryeh Kontorovich
Stable Sample Compression Schemes: New Applications and an Optimal SVM Margin Bound. CoRR (2020)
Yair Ashlagi, Lee-Ad Gottlieb, Aryeh Kontorovich
Functions with average smoothness: structure, algorithms, and learning. CoRR (2020)
Klim Efremenko, Aryeh Kontorovich, Moshe Noivirt
Fast and Bayes-consistent nearest neighbors. AISTATS (2020)
Doron Cohen, Aryeh Kontorovich, Geoffrey Wolfer
Learning discrete distributions with infinite support. NeurIPS (2020)
Aryeh Kontorovich, Gergely Neu
Algorithmic Learning Theory 2020: Preface. ALT (2020)
Lee-Ad Gottlieb, Eran Kaufman, Aryeh Kontorovich
Apportioned Margin Approach for Cost Sensitive Large Margin Classifiers. CoRR (2020)
Daniel Berend, Aryeh Kontorovich, Lev Reyzin, Thomas Robinson
On Biased Random Walks, Corrupted Intervals, and Learning Under Adversarial Design. CoRR (2020)
Ariel Avital, Klim Efremenko, Aryeh Kontorovich, David Toplin, Bo Waggoner
Non-parametric Binary regression in metric spaces with KL loss. CoRR (2020)
Geoffrey Wolfer, Aryeh Kontorovich
Minimax Testing of Identity to a Reference Ergodic Markov Chain. AISTATS (2020)
Daniel Berend, Aryeh Kontorovich, Lev Reyzin, Thomas Robinson
On biased random walks, corrupted intervals, and learning under adversarial design. Ann. Math. Artif. Intell. 88 (8) (2020)
Lee-Ad Gottlieb, Eran Kaufman, Aryeh Kontorovich, Gabriel Nivasch, Ofir Pele
Nested Barycentric Coordinate System as an Explicit Feature Map. CoRR (2020)
Steve Hanneke, Aryeh Kontorovich, Sivan Sabato, Roi Weiss
Universal Bayes Consistency in Metric Spaces. ITA (2020)
Geoffrey Wolfer, Aryeh Kontorovich
Minimax Learning of Ergodic Markov Chains. ALT (2019)
Idan Attias, Aryeh Kontorovich, Yishay Mansour
Improved Generalization Bounds for Robust Learning. ALT (2019)
Klim Efremenko, Aryeh Kontorovich, Moshe Noivirt
Fast and Bayes-consistent nearest neighbors. CoRR (2019)
Eyal Gutflaish, Aryeh Kontorovich, Sivan Sabato, Ofer Biller, Oded Sofer
Temporal Anomaly Detection: Calibrating the Surprise. AAAI (2019)
Geoffrey Wolfer, Aryeh Kontorovich
Estimating the Mixing Time of Ergodic Markov Chains. CoRR (2019)
Steve Hanneke, Aryeh Kontorovich
Optimality of SVM: Novel proofs and tighter bounds. Theor. Comput. Sci. 796 (2019)
Geoffrey Wolfer, Aryeh Kontorovich
Estimating the Mixing Time of Ergodic Markov Chains. COLT (2019)
Armin Biess, Aryeh Kontorovich, Yury Makarychev, Hanan Zaichyk
Regression via Kirszbraun Extension with Applications to Imitation Learning. CoRR (2019)
Steve Hanneke, Aryeh Kontorovich, Menachem Sadigurschi
Sample Compression for Real-Valued Learners. ALT (2019)
Steve Hanneke, Aryeh Kontorovich, Sivan Sabato, Roi Weiss
Universal Bayes consistency in metric spaces. CoRR (2019)
Steve Hanneke, Aryeh Kontorovich
A Sharp Lower Bound for Agnostic Learning with Sample Compression Schemes. ALT (2019)
Idan Attias, Aryeh Kontorovich, Yishay Mansour
Improved generalization bounds for robust learning. CoRR (2018)
Geoffrey Wolfer, Aryeh Kontorovich
Minimax Learning of Ergodic Markov Chains. CoRR (2018)
Steve Hanneke, Aryeh Kontorovich, Menachem Sadigurschi
Agnostic Sample Compression for Linear Regression. CoRR (2018)
Steve Hanneke, Aryeh Kontorovich, Menachem Sadigurschi
Sample Compression for Real-Valued Learners. CoRR (2018)
Lee-Ad Gottlieb, Aryeh Kontorovich, Pinhas Nisnevitch
Near-Optimal Sample Compression for Nearest Neighbors. IEEE Trans. Inf. Theory 64 (6) (2018)
Lee-Ad Gottlieb, Eran Kaufman, Aryeh Kontorovich, Gabriel Nivasch
Learning convex polytopes with margin. CoRR (2018)
Steve Hanneke, Aryeh Kontorovich
A New Lower Bound for Agnostic Learning with Sample Compression Schemes. CoRR (2018)
Lee-Ad Gottlieb, Eran Kaufman, Aryeh Kontorovich, Gabriel Nivasch
Learning convex polytopes with margin. NeurIPS (2018)
Matan Levi, Yair Allouche, Aryeh Kontorovich
Advanced Analytics for Connected Car Cybersecurity. VTC Spring (2018)
Aryeh Kontorovich, Sivan Sabato, Roi Weiss
Nearest-Neighbor Sample Compression: Efficiency, Consistency, Infinite Dimensions. NIPS (2017)
Aryeh Kontorovich, Sivan Sabato, Roi Weiss
Nearest-Neighbor Sample Compression: Efficiency, Consistency, Infinite Dimensions. CoRR (2017)
Lee-Ad Gottlieb, Aryeh Kontorovich, Robert Krauthgamer
Efficient Regression in Metric Spaces via Approximate Lipschitz Extension. IEEE Trans. Inf. Theory 63 (8) (2017)
Lee-Ad Gottlieb, Aryeh Kontorovich, Pinhas Nisnevitch
Nearly optimal classification for semimetrics. J. Mach. Learn. Res. 18 (2017)
Dan Gutfreund, Aryeh Kontorovich, Ran Levy, Michal Rosen-Zvi
Boosting conditional probability estimators. Ann. Math. Artif. Intell. 79 (1-3) (2017)
Daniel J. Hsu, Aryeh Kontorovich, David A. Levin, Yuval Peres, Csaba Szepesvári
Mixing time estimation in reversible Markov chains from a single sample path. CoRR (2017)
Eyal Gutflaish, Aryeh Kontorovich, Sivan Sabato, Ofer Biller, Oded Sofer
Temporal anomaly detection: calibrating the surprise. CoRR (2017)
Daniel Berend, Aryeh Kontorovich, Gil Zagdanski
The Expected Missing Mass under an Entropy Constraint. Entropy 19 (7) (2017)
Matan Levi, Yair Allouche, Aryeh Kontorovich
Advanced Analytics for Connected Cars Cyber Security. CoRR (2017)
Aryeh Kontorovich, Sivan Sabato, Ruth Urner
Active Nearest-Neighbor Learning in Metric Spaces. J. Mach. Learn. Res. 18 (2017)
Aryeh Kontorovich, Maxim Raginsky
Concentration of measure without independence: a unified approach via the martingale method. CoRR (2016)
Lee-Ad Gottlieb, Aryeh Kontorovich, Pinhas Nisnevitch
Nearly Optimal Classification for Semimetrics. AISTATS (2016)
Lee-Ad Gottlieb, Aryeh Kontorovich, Robert Krauthgamer
Adaptive metric dimensionality reduction. Theor. Comput. Sci. 620 (2016)
Aryeh Kontorovich, Iosif Pinelis
Exact Lower Bounds for the Agnostic Probably-Approximately-Correct (PAC) Machine Learning Model. CoRR (2016)
Aryeh Kontorovich, Sivan Sabato, Ruth Urner
Active Nearest-Neighbor Learning in Metric Spaces. CoRR (2016)
Aryeh Kontorovich, Sivan Sabato, Ruth Urner
Active Nearest-Neighbor Learning in Metric Spaces. NIPS (2016)
Daniel Berend, Aryeh Kontorovich
The state complexity of random DFAs. Theor. Comput. Sci. 652 (2016)
Daniel Berend, Aryeh Kontorovich
A finite sample analysis of the Naive Bayes classifier. J. Mach. Learn. Res. 16 (2015)
Daniel J. Hsu, Aryeh Kontorovich, Csaba Szepesvári
Mixing Time Estimation in Reversible Markov Chains from a Single Sample Path. CoRR (2015)
Daniel J. Hsu, Aryeh Kontorovich, Csaba Szepesvári
Mixing Time Estimation in Reversible Markov Chains from a Single Sample Path. NIPS (2015)
Aryeh Kontorovich, Roi Weiss
A Bayes consistent 1-NN classifier. AISTATS (2015)
Daniel Gordon, Danny Hendler, Aryeh Kontorovich, Lior Rokach
Local-shapelets for fast classification of spectrographic measurements. Expert Syst. Appl. 42 (6) (2015)
Lee-Ad Gottlieb, Aryeh Kontorovich
Nearly optimal classification for semimetrics. CoRR (2015)
Dan Gutfreund, Aryeh Kontorovich, Ran Levy, Michal Rosen-Zvi
Boosting Conditional Probability Estimators. ISAIM (2014)
Daniel Berend, Peter Harremoës, Aryeh Kontorovich
Minimum KL-Divergence on Complements of $L_{1}$ Balls. IEEE Trans. Inf. Theory 60 (6) (2014)
Lee-Ad Gottlieb, Aryeh Kontorovich
Near-optimal sample compression for nearest neighbors. CoRR (2014)
Aryeh Kontorovich, Roi Weiss
Maximum Margin Multiclass Nearest Neighbors. CoRR (2014)
Aryeh Kontorovich, Roi Weiss
A Bayes consistent 1-NN classifier. CoRR (2014)
Aryeh Kontorovich, Roi Weiss
Maximum Margin Multiclass Nearest Neighbors. ICML (2014)
Aryeh Kontorovich, Ari Trachtenberg
Deciding unique decodability of bigram counts via finite automata. J. Comput. Syst. Sci. 80 (2) (2014)
Aryeh Kontorovich, Roi Weiss
Uniform Chernoff and Dvoretzky-Kiefer-Wolfowitz-Type Inequalities for Markov Chains and Related Processes. J. Appl. Probab. 51 (4) (2014)
Daniel Berend, Aryeh Kontorovich
Consistency of weighted majority votes. NIPS (2014)
Lee-Ad Gottlieb, Aryeh Kontorovich, Robert Krauthgamer
Efficient Classification for Metric Data. IEEE Trans. Inf. Theory 60 (9) (2014)
Lee-Ad Gottlieb, Aryeh Kontorovich, Pinhas Nisnevitch
Near-optimal sample compression for nearest neighbors. NIPS (2014)
Aryeh Kontorovich
Concentration in unbounded metric spaces and algorithmic stability. ICML (2014)

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Publication Activity (10 Years)

Top Topics

Top Venues

Aryeh Kontorovich