The Limits of Maxing, Ranking, and Preference Learning. - Moein Falahatgar; Ayush Jain 001; Alon Orlitsky; Venkatadheeraj Pichapati; Vaishakh Ravindrakumar (ICML, 2018)

Effective Memory Shrinkage in Estimation. - Ayush Jain 001; Himanshu Tyagi (ISIT, 2018)

Robust Learning of Discrete Distributions from Batches. - Ayush Jain 001; Alon Orlitsky (CoRR, 2019)

Optimal Robust Learning of Discrete Distributions from Batches. - Ayush Jain 001; Alon Orlitsky (ICML, 2020)

SURF: A Simple, Universal, Robust, Fast Distribution Learning Algorithm. - Yi Hao; Ayush Jain 001; Alon Orlitsky; Vaishakh Ravindrakumar (NeurIPS, 2020)

Linear-Sample Learning of Low-Rank Distributions. - Ayush Jain 001; Alon Orlitsky (NeurIPS, 2020)

A General Method for Robust Learning from Batches. - Ayush Jain 001; Alon Orlitsky (NeurIPS, 2020)

Robust Density Estimation from Batches: The Best Things in Life are (Nearly) Free. - Ayush Jain 001; Alon Orlitsky (ICML, 2021)

Robust Estimation for Random Graphs. - Jayadev Acharya; Ayush Jain 001; Gautam Kamath 001; Ananda Theertha Suresh; Huanyu Zhang (COLT, 2022)

The Price of Tolerance in Distribution Testing. - Clément L. Canonne; Ayush Jain 001; Gautam Kamath 001; Jerry Li 001 (COLT, 2022)

TURF: Two-Factor, Universal, Robust, Fast Distribution Learning Algorithm. - Yi Hao; Ayush Jain 001; Alon Orlitsky; Vaishakh Ravindrakumar (ICML, 2022)

Robust estimation algorithms don't need to know the corruption level. - Ayush Jain 001; Alon Orlitsky; Vaishakh Ravindrakumar (CoRR, 2022)

Efficient List-Decodable Regression using Batches. - Abhimanyu Das; Ayush Jain 001; Weihao Kong; Rajat Sen (ICML, 2023)

Learning in the Presence of Adversaries - Ayush Jain 001 (2023)

Linear Regression using Heterogeneous Data Batches. - Ayush Jain 001; Rajat Sen; Weihao Kong; Abhimanyu Das; Alon Orlitsky (NeurIPS, 2024)