← Authors

Aryeh Kontorovich

44 papers · 2013–2025 · 7 conferences · across top CS/AI conferences

Achievements

πŸƒ Academic Marathon (12) 🐝 Cross-Pollinator (12) 🌈 Renaissance Researcher (8) 🌍 Conference Polyglot (7) πŸ—ΊοΈ Taxonomy Completionist (12) 🧭 Keyword Pioneer 🐣 Hot Topic Early Bird πŸŒ‰ Interdisciplinary Bridge πŸ”¬ Deep Specialist (24) πŸ† Keyword Champion (2) πŸ—ƒοΈ Keyword Collector (162) πŸš€ Conference Pioneer πŸ’Ž Century Club (44) πŸ”₯ Unstoppable (13) πŸ“ˆ Trend Setter ⚑ Prolific Year (6)

Conferences

NIPS (10) ALT (8) JMLR (8) COLT (6) ICML (6) AISTATS (5) AAAI (1)

Top co-authors

Lee-Ad Gottlieb (7) Steve Hanneke (6) Doron Cohen (6) Geoffrey Wolfer (5) Roi Weiss (5) Idan Attias (4) Sivan Sabato (4) Menachem Sadigurschi (3) Pinhas Nisnevitch (3) Gabriel Nivasch (2)

Research topics

Differential Privacy (1)

Keywords

generalization bound (11) sample compression (9) metric space (7) learning theory (6) sample complexity (5) nearest neighbor (4) nearest neighbor classifier (4) pac learning (4) bayes consistency (3) average smoothness (3) rademacher complexity (3) minimax rate (3) margin-based learning (3) concentration inequality (3) uniform convergence (3) covering number (3) information theory (2) function learning (2) stochastic process (2) regression analysis (2)

Papers

Distribution Estimation under the Infinity Norm JMLR 2025
The Empirical Mean is Minimax Optimal for Local Glivenko-Cantelli ICML 2025
Sharp bounds on aggregate expert error ALT 2025
Efficient Agnostic Learning with Average Smoothness ALT 2024
Correlated Binomial Process COLT 2024
Agnostic Sample Compression Schemes for Regression ICML 2024
Fat-Shattering Dimension of k-fold Aggregations JMLR 2024
Functions with average smoothness: structure, algorithms, and learning JMLR 2024
Local Glivenko-Cantelli COLT 2023
Near-optimal learning with average HΓΆlder smoothness NIPS 2023
Open problem: log(n) factor in "Local Glivenko-Cantelli" COLT 2023
Adaptive Data Analysis with Correlated Observations ICML 2022
Learning with metric losses COLT 2022
Improved Generalization Bounds for Adversarially Robust Learning JMLR 2022
Stable Sample Compression Schemes: New Applications and an Optimal SVM Margin Bound ALT 2021
Nested Barycentric Coordinate System as an Explicit Feature Map AISTATS 2021
Dimension-free empirical entropy estimation NIPS 2021
Functions with average smoothness: structure, algorithms, and learning COLT 2021
Fast and Bayes-consistent nearest neighbors AISTATS 2020
Algorithmic Learning Theory 2020: Preface ALT 2020
Learning discrete distributions with infinite support NIPS 2020
Minimax Testing of Identity to a Reference Ergodic Markov Chain AISTATS 2020
Temporal Anomaly Detection: Calibrating the Surprise AAAI 2019
Minimax Learning of Ergodic Markov Chains ALT 2019
Estimating the Mixing Time of Ergodic Markov Chains COLT 2019
Sample Compression for Real-Valued Learners ALT 2019
A Sharp Lower Bound for Agnostic Learning with Sample Compression Schemes ALT 2019
Improved Generalization Bounds for Robust Learning ALT 2019
Active Nearest-Neighbor Learning in Metric Spaces JMLR 2018
Learning convex polytopes with margin NIPS 2018
Nearest-Neighbor Sample Compression: Efficiency, Consistency, Infinite Dimensions NIPS 2017
Nearly optimal classification for semimetrics JMLR 2017
Active Nearest-Neighbor Learning in Metric Spaces NIPS 2016
Nearly Optimal Classification for Semimetrics AISTATS 2016
A Bayes consistent 1-NN classifier AISTATS 2015
A Finite Sample Analysis of the Naive Bayes Classifier JMLR 2015
Mixing Time Estimation in Reversible Markov Chains from a Single Sample Path NIPS 2015
Near-optimal sample compression for nearest neighbors NIPS 2014
Maximum Margin Multiclass Nearest Neighbors ICML 2014
Concentration in unbounded metric spaces and algorithmic stability ICML 2014
Consistency of weighted majority votes NIPS 2014
On learning parametric-output HMMs ICML 2013
Predictive PAC Learning and Process Decompositions NIPS 2013
On the Learnability of Shuffle Ideals JMLR 2013