Aryeh Kontorovich
44 papers · 2013–2025 · 7 conferences · across top CS/AI conferences
Achievements
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π£ Hot Topic Early Bird π Interdisciplinary Bridge πΊοΈ Taxonomy Completionist (12) π§ Keyword Pioneer π Conference Polyglot (7)
π§
Keyword Pioneer
π£
Hot Topic Early Bird
π
Interdisciplinary Bridge
π¬
Deep Specialist
(24)
π
Keyword Champion
(2)
ποΈ
Keyword Collector
(162)
π
Conference Pioneer
π
Century Club
(44)
π₯
Unstoppable
(13)
π
Trend Setter
β‘
Prolific Year
(6)
Conferences
NIPS (10)
ALT (8)
JMLR (8)
COLT (6)
ICML (6)
AISTATS (5)
AAAI (1)
Top co-authors
Research topics
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