Hi, I'm a Postdoctoral Researcher at UC Berkeley and Rice University, hosted by --jelaninelson-- and --vladimirbraverman--. I'm starting as an Assistant Professor at Texas A&M University in Fall 2023. My research lies at the intersections of theoretical computer science, data science, and machine learning. In particular, I am currently interested in numerical linear algebra, streaming algorithms, and differential privacy. Here are some of my favorite links . I'm looking for a postdoc broadly working in diff
The problem of selecting a small-size representative summary of a large dataset is a cornerstone of machine learning, optimization and data science. Motivated by applications to recommendation systems and other scenarios with query-limited access to vast amounts of data, we propose a new rigorous algorithmic framework for a standard formulation of this problem as a submodular maximization subject to a linear (knapsack) constraint. Our framework is based on augmenting all partial Greedy solutions with the be
Previous work showed empirically that large neural networks can be significantly reduced in size while preserving their accuracy. Model compression became a central research topic, as it is crucial for deployment of neural networks on devices with limited computational and memory resources. The majority of the compression methods are based on heuristics and offer no worst-case guarantees on the trade-off between the compression rate and the approximation error for an arbitrarily new sample. We propose the f
Grigory Yaroslavtsev
Workshop on Algorithms for Large Data (Online) 2021
Fred Zhang's Homepage
Grigory Yaroslavtsev
Grigory Yaroslavtsev
Jelani Nelson
Shubhang