18 citations · 44 across the 12 of their papers we have counts for
17 papers
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…
Profile Entropy: A Fundamental Measure for the Learnability and Compressibility of Discrete Distributions
Yi Hao, Alon Orlitsky
The profile of a sample is the multiset of its symbol frequencies. We show that for samples of discrete distributions, profile entropy is a fundamental measure unifying the concept…
A General Method for Robust Learning from Batches
Ayush Jain, Alon Orlitsky
In many applications, data is collected in batches, some of which are corrupt or even adversarial. Recent work derived optimal robust algorithms for estimating discrete distributio…
SURF: A Simple, Universal, Robust, Fast Distribution Learning Algorithm
Yi Hao, Ayush Jain, Alon Orlitsky +1
Sample- and computationally-efficient distribution estimation is a fundamental tenet in statistics and machine learning. We present SURF, an algorithm for approximating distributio…
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…