no code implementations • 18 Mar 2024 • Gregory Dexter, Christos Boutsikas, Linkai Ma, Ilse C. F. Ipsen, Petros Drineas
Motivated by the popularity of stochastic rounding in the context of machine learning and the training of large-scale deep neural network models, we consider stochastic nearness rounding of real matrices $\mathbf{A}$ with many more rows than columns.
no code implementations • 22 Jan 2024 • Gregory Dexter, Borja Ocejo, Sathiya Keerthi, Aman Gupta, Ayan Acharya, Rajiv Khanna
In this paper, we delve deeper into the relationship between linear stability and sharpness.
no code implementations • 24 Mar 2023 • Gregory Dexter, Rajiv Khanna, Jawad Raheel, Petros Drineas
We present novel bounds for coreset construction, feature selection, and dimensionality reduction for logistic regression.
no code implementations • 19 Feb 2023 • Kayhan Behdin, Qingquan Song, Aman Gupta, Sathiya Keerthi, Ayan Acharya, Borja Ocejo, Gregory Dexter, Rajiv Khanna, David Durfee, Rahul Mazumder
Modern deep learning models are over-parameterized, where different optima can result in widely varying generalization performance.
no code implementations • NeurIPS 2021 • Gregory Dexter, Kevin Bello, Jean Honorio
Inverse Reinforcement Learning (IRL) is the problem of finding a reward function which describes observed/known expert behavior.