Ayush Jain

Affiliations:
  • University of California, San Diego, Department of Electrical and Computer Engineering, La Jolla, CA, USA


According to our database1, Ayush Jain authored at least 14 papers between 2018 and 2023.

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Bibliography

2023
Linear Regression using Heterogeneous Data Batches.
CoRR, 2023

Efficient List-Decodable Regression using Batches.
Proceedings of the International Conference on Machine Learning, 2023

2022
Robust estimation algorithms don't need to know the corruption level.
CoRR, 2022

TURF: Two-Factor, Universal, Robust, Fast Distribution Learning Algorithm.
Proceedings of the International Conference on Machine Learning, 2022

The Price of Tolerance in Distribution Testing.
Proceedings of the Conference on Learning Theory, 2-5 July 2022, London, UK., 2022

Robust Estimation for Random Graphs.
Proceedings of the Conference on Learning Theory, 2-5 July 2022, London, UK., 2022

2021
Robust Density Estimation from Batches: The Best Things in Life are (Nearly) Free.
Proceedings of the 38th International Conference on Machine Learning, 2021

2020
A General Method for Robust Learning from Batches.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Linear-Sample Learning of Low-Rank Distributions.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

SURF: A Simple, Universal, Robust, Fast Distribution Learning Algorithm.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Optimal Robust Learning of Discrete Distributions from Batches.
Proceedings of the 37th International Conference on Machine Learning, 2020

2019
Robust Learning of Discrete Distributions from Batches.
CoRR, 2019

2018
Effective Memory Shrinkage in Estimation.
Proceedings of the 2018 IEEE International Symposium on Information Theory, 2018

The Limits of Maxing, Ranking, and Preference Learning.
Proceedings of the 35th International Conference on Machine Learning, 2018


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