23 citations · 94 across the 24 of their papers we have counts for
8 papers · 1 filter
Linear Regression using Heterogeneous Data Batches
Ayush Jain, Rajat Sen, Weihao Kong +2
In many learning applications, data are collected from multiple sources, each providing a \emph{batch} of samples that by itself is insufficient to learn its input-output relations…
Robust estimation algorithms don't need to know the corruption level
Ayush Jain, Alon Orlitsky, Vaishakh Ravindrakumar
Real data are rarely pure. Hence the past half-century has seen great interest in robust estimation algorithms that perform well even when part of the data is corrupt. However, the…
Linear-Sample Learning of Low-Rank Distributions
Ayush Jain, Alon Orlitsky
Many latent-variable applications, including community detection, collaborative filtering, genomic analysis, and NLP, model data as generated by low-rank matrices. Yet despite cons…
Optimal Robust Learning of Discrete Distributions from Batches
Ayush Jain, Alon Orlitsky
Many applications, including natural language processing, sensor networks, collaborative filtering, and federated learning, call for estimating discrete distributions from data col…
Unified Sample-Optimal Property Estimation in Near-Linear Time
Yi Hao, Alon Orlitsky
We consider the fundamental learning problem of estimating properties of distributions over large domains. Using a novel piecewise-polynomial approximation technique, we derive the…
On Learning Markov Chains
Yi Hao, Alon Orlitsky, Venkatadheeraj Pichapati
The problem of estimating an unknown discrete distribution from its samples is a fundamental tenet of statistical learning. Over the past decade, it attracted significant research…
Maximum Selection and Ranking under Noisy Comparisons
Moein Falahatgar, Alon Orlitsky, Venkatadheeraj Pichapati +1
We consider -PAC maximum-selection and ranking for general probabilistic models whose comparisons probabilities satisfy strong stochastic transitivity and stochastic triangl…
Near-optimal-sample estimators for spherical Gaussian mixtures
Jayadev Acharya, Ashkan Jafarpour, Alon Orlitsky +1
Statistical and machine-learning algorithms are frequently applied to high-dimensional data. In many of these applications data is scarce, and often much more costly than computati…