ML Anthology

Kontorovich, Aryeh

48 publications

JMLR 2025 Distribution Estimation Under the Infinity Norm Aryeh Kontorovich, Amichai Painsky
ALT 2025 Sharp Bounds on Aggregate Expert Error Aryeh Kontorovich, Ariel Avital
ICML 2025 The Empirical Mean Is Minimax Optimal for Local Glivenko-Cantelli Doron Cohen, Aryeh Kontorovich, Roi Weiss
ICML 2024 Agnostic Sample Compression Schemes for Regression Idan Attias, Steve Hanneke, Aryeh Kontorovich, Menachem Sadigurschi
COLT 2024 Correlated Binomial Process Moïse Blanchard, Doron Cohen, Aryeh Kontorovich
ALT 2024 Efficient Agnostic Learning with Average Smoothness Steve Hanneke, Aryeh Kontorovich, Guy Kornowski
JMLR 2024 Fat-Shattering Dimension of K-Fold Aggregations Idan Attias, Aryeh Kontorovich
JMLR 2024 Functions with Average Smoothness: Structure, Algorithms, and Learning Yair Ashlagi, Lee-Ad Gottlieb, Aryeh Kontorovich
MLJ 2024 Nested Barycentric Coordinate System as an Explicit Feature mAP for Polyhedra Approximation and Learning Tasks Lee-Ad Gottlieb, Eran Kaufman, Aryeh Kontorovich, Gabriel Nivasch, Ofir Pele
COLT 2023 Local Glivenko-Cantelli Doron Cohen, Aryeh Kontorovich
NeurIPS 2023 Near-Optimal Learning with Average Hölder Smoothness Guy Kornowski, Steve Hanneke, Aryeh Kontorovich
COLT 2023 Open Problem: Log(n) Factor in "Local Glivenko-Cantelli" Doron Cohen, Aryeh Kontorovich
ICML 2022 Adaptive Data Analysis with Correlated Observations Aryeh Kontorovich, Menachem Sadigurschi, Uri Stemmer
TMLR 2022 Domain Invariant Adversarial Learning Matan Levi, Idan Attias, Aryeh Kontorovich
JMLR 2022 Improved Generalization Bounds for Adversarially Robust Learning Idan Attias, Aryeh Kontorovich, Yishay Mansour
COLT 2022 Learning with Metric Losses Dan Tsir Cohen, Aryeh Kontorovich
AISTATS 2021 Nested Barycentric Coordinate System as an Explicit Feature mAP Lee-Ad Gottlieb, Eran Kaufman, Aryeh Kontorovich, Gabriel Nivasch, Ofir Pele
NeurIPS 2021 Dimension-Free Empirical Entropy Estimation Doron Cohen, Aryeh Kontorovich, Aaron Koolyk, Geoffrey Wolfer
COLT 2021 Functions with Average Smoothness: Structure, Algorithms, and Learning Yair Ashlagi, Lee-Ad Gottlieb, Aryeh Kontorovich
ALT 2021 Stable Sample Compression Schemes: New Applications and an Optimal SVM Margin Bound Steve Hanneke, Aryeh Kontorovich
ALT 2020 Algorithmic Learning Theory 2020: Preface Aryeh Kontorovich, Gergely Neu
AISTATS 2020 Fast and Bayes-Consistent Nearest Neighbors Klim Efremenko, Aryeh Kontorovich, Moshe Noivirt
NeurIPS 2020 Learning Discrete Distributions with Infinite Support Doron Cohen, Aryeh Kontorovich, Geoffrey Wolfer
AISTATS 2020 Minimax Testing of Identity to a Reference Ergodic Markov Chain Geoffrey Wolfer, Aryeh Kontorovich
ALT 2019 A Sharp Lower Bound for Agnostic Learning with Sample Compression Schemes Steve Hanneke, Aryeh Kontorovich
COLT 2019 Estimating the Mixing Time of Ergodic Markov Chains Geoffrey Wolfer, Aryeh Kontorovich
ALT 2019 Improved Generalization Bounds for Robust Learning Idan Attias, Aryeh Kontorovich, Yishay Mansour
ALT 2019 Minimax Learning of Ergodic Markov Chains Geoffrey Wolfer, Aryeh Kontorovich
ALT 2019 Sample Compression for Real-Valued Learners Steve Hanneke, Aryeh Kontorovich, Menachem Sadigurschi
AAAI 2019 Temporal Anomaly Detection: Calibrating the Surprise Eyal Gutflaish, Aryeh Kontorovich, Sivan Sabato, Ofer Biller, Oded Sofer
NeurIPS 2018 Learning Convex Polytopes with Margin Lee-Ad Gottlieb, Eran Kaufman, Aryeh Kontorovich, Gabriel Nivasch
NeurIPS 2017 Nearest-Neighbor Sample Compression: Efficiency, Consistency, Infinite Dimensions Aryeh Kontorovich, Sivan Sabato, Roi Weiss
JMLR 2017 Nearly Optimal Classification for Semimetrics Lee-Ad Gottlieb, Aryeh Kontorovich, Pinhas Nisnevitch
NeurIPS 2016 Active Nearest-Neighbor Learning in Metric Spaces Aryeh Kontorovich, Sivan Sabato, Ruth Urner
AISTATS 2016 Nearly Optimal Classification for Semimetrics Lee-Ad Gottlieb, Aryeh Kontorovich, Pinhas Nisnevitch
AISTATS 2015 A Bayes Consistent 1-NN Classifier Aryeh Kontorovich, Roi Weiss
JMLR 2015 A Finite Sample Analysis of the Naive Bayes Classifier Daniel Berend, Aryeh Kontorovich
NeurIPS 2015 Mixing Time Estimation in Reversible Markov Chains from a Single Sample Path Daniel J. Hsu, Aryeh Kontorovich, Csaba Szepesvari
ICML 2014 Concentration in Unbounded Metric Spaces and Algorithmic Stability Aryeh Kontorovich
NeurIPS 2014 Consistency of Weighted Majority Votes Daniel Berend, Aryeh Kontorovich
ICML 2014 Maximum Margin Multiclass Nearest Neighbors Aryeh Kontorovich, Roi Weiss
NeurIPS 2014 Near-Optimal Sample Compression for Nearest Neighbors Lee-Ad Gottlieb, Aryeh Kontorovich, Pinhas Nisnevitch
ALT 2013 Adaptive Metric Dimensionality Reduction Lee-Ad Gottlieb, Aryeh Kontorovich, Robert Krauthgamer
MLJ 2013 Exploiting Label Dependencies for Improved Sample Complexity Lena Chekina, Dan Gutfreund, Aryeh Kontorovich, Lior Rokach, Bracha Shapira
ICML 2013 On Learning Parametric-Output HMMs Aryeh Kontorovich, Boaz Nadler, Roi Weiss
JMLR 2013 On the Learnability of Shuffle Ideals Dana Angluin, James Aspnes, Sarah Eisenstat, Aryeh Kontorovich
NeurIPS 2013 Predictive PAC Learning and Process Decompositions Cosma Shalizi, Aryeh Kontorovich
ALT 2012 On the Learnability of Shuffle Ideals Dana Angluin, James Aspnes, Aryeh Kontorovich