Alon Orlitsky
34 papers · 2003–2024 · 5 conferences · across top CS/AI conferences
Achievements
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π§ Keyword Pioneer π Conference Polyglot (5) π Interdisciplinary Bridge πΊοΈ Taxonomy Completionist (10) π Academic Marathon (21)
π
Renaissance Researcher
(6)
π§
Keyword Pioneer
π£
Hot Topic Early Bird
π€
Dynamic Duo
(11)
π
Keyword Champion
(2)
π¬
Deep Specialist
(20)
π
Century Club
(34)
π
Conference Pioneer
β‘
Prolific Year
(8)
ποΈ
Keyword Collector
(134)
π
Trend Setter
π₯
Unstoppable
(12)
Conferences
NIPS (15)
ICML (10)
COLT (5)
AISTATS (2)
JMLR (2)
Top co-authors
Research topics
Keywords
sample complexity
(13)
distribution estimation
(11)
distribution learning
(5)
density estimation
(5)
distribution testing
(4)
discrete distribution
(4)
kl divergence
(4)
statistical estimation
(4)
information theory
(3)
batch learning
(3)
good-turing estimator
(3)
maximum selection
(3)
hypothesis testing
(3)
statistical learning
(3)
minimax optimality
(3)
stochastic transitivity
(3)
property estimation
(3)
entropy estimation
(2)
pairwise comparison
(2)
robust estimation
(2)
Papers
Linear Regression using Heterogeneous Data Batches
NIPS 2024
TURF: Two-Factor, Universal, Robust, Fast Distribution Learning Algorithm
ICML 2022
Robust Density Estimation from Batches: The Best Things in Life are (Nearly) Free
ICML 2021
Compressed Maximum Likelihood
ICML 2021
Optimal Sequential Maximization: One Interview is Enough!
ICML 2020
Profile Entropy: A Fundamental Measure for the Learnability and Compressibility of Distributions
NIPS 2020
SURF: A Simple, Universal, Robust, Fast Distribution Learning Algorithm
NIPS 2020
Linear-Sample Learning of Low-Rank Distributions
NIPS 2020
A General Method for Robust Learning from Batches
NIPS 2020
Optimal Robust Learning of Discrete Distributions from Batches
ICML 2020
Data Amplification: Instance-Optimal Property Estimation
ICML 2020
Towards Competitive N-gram Smoothing
AISTATS 2020
Unified Sample-Optimal Property Estimation in Near-Linear Time
NIPS 2019
The Broad Optimality of Profile Maximum Likelihood
NIPS 2019
Doubly-Competitive Distribution Estimation
ICML 2019
The Limits of Maxing, Ranking, and Preference Learning
ICML 2018
Maximum Selection and Sorting with Adversarial Comparators
JMLR 2018
Data Amplification: A Unified and Competitive Approach to Property Estimation
NIPS 2018
On Learning Markov Chains
NIPS 2018
The power of absolute discounting: all-dimensional distribution estimation
NIPS 2017
Maxing and Ranking with Few Assumptions
NIPS 2017
A Unified Maximum Likelihood Approach for Estimating Symmetric Properties of Discrete Distributions
ICML 2017
Maximum Selection and Ranking under Noisy Comparisons
ICML 2017
Near-Optimal Smoothing of Structured Conditional Probability Matrices
NIPS 2016
On Learning Distributions from their Samples
COLT 2015
Faster Algorithms for Testing under Conditional Sampling
COLT 2015
Competitive Distribution Estimation: Why is Good-Turing Good
NIPS 2015
Near-Optimal-Sample Estimators for Spherical Gaussian Mixtures
NIPS 2014
A Competitive Test for Uniformity of Monotone Distributions
AISTATS 2013
Optimal Probability Estimation with Applications to Prediction and Classification
COLT 2013
Tight Bounds on Profile Redundancy and Distinguishability
NIPS 2012
Competitive Classification and Closeness Testing
COLT 2012
Competitive Closeness Testing
COLT 2011
On Nearest-Neighbor Error-Correcting Output Codes with Application to All-Pairs Multiclass Support Vector Machines
JMLR 2003