← Authors

Alon Orlitsky

34 papers · 2003–2024 · 5 conferences · across top CS/AI conferences

Achievements

🌍 Conference Polyglot (5) πŸƒ Academic Marathon (21) 🐝 Cross-Pollinator (12) πŸŒ‰ Interdisciplinary Bridge πŸ—ΊοΈ Taxonomy Completionist (10) 🌈 Renaissance Researcher (6) 🧭 Keyword Pioneer 🐣 Hot Topic Early Bird 🀝 Dynamic Duo (11) πŸ† Keyword Champion (2) πŸ”¬ Deep Specialist (20) πŸ’Ž Century Club (34) πŸš€ Conference Pioneer ⚑ Prolific Year (8) πŸ—ƒοΈ Keyword Collector (134) πŸ“ˆ Trend Setter πŸ”₯ Unstoppable (12)

Conferences

NIPS (15) ICML (10) COLT (5) AISTATS (2) JMLR (2)

Top co-authors

Yi Hao (11) Moein Falahatgar (9) Ananda Theertha Suresh (9) Jayadev Acharya (8) Ayush Jain (8) Venkatadheeraj Pichapati (8) Ashkan Jafarpour (7) Hirakendu Das (4) Vaishakh Ravindrakumar (4) Shengjun Pan (2)

Research topics

Statistics (4) Probability (1)

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