5 citations · 10 across the 9 of their papers we have counts for
9 papers
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…
Efficient List-Decodable Regression using Batches
Abhimanyu Das, Ayush Jain, Weihao Kong +1
We begin the study of list-decodable linear regression using batches. In this setting only an fraction of the batches are genuine. Each genuine batch contains …
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…
TURF: A Two-factor, Universal, Robust, Fast Distribution Learning Algorithm
Yi Hao, Ayush Jain, Alon Orlitsky +1
Approximating distributions from their samples is a canonical statistical-learning problem. One of its most powerful and successful modalities approximates every distribution to an…
Robust Estimation for Random Graphs
Jayadev Acharya, Ayush Jain, Gautam Kamath +2
We study the problem of robustly estimating the parameter of an Erdős-Rényi random graph on nodes, where a fraction of nodes may be adversarially corrupted. After showi…
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…