1 citations · 1 across the 7 of their papers we have counts for
7 papers
Spectral density estimation for normal matrices
Cameron Musco, Christopher Musco, Rikhav Shah +2
The spectral density estimation problem asks for an algorithm that, given an matrix , outputs a probability measure that is a good approximation to the uniform distr…
Matrix Product Sketching via Coordinated Sampling
Majid Daliri, Juliana Freire, Danrong Li +1
We revisit the well-studied problem of approximating a matrix product, , based on small space sketches and $\mathcal{S}(\mathbf{B}…
Coupling without Communication and Drafter-Invariant Speculative Decoding
Majid Daliri, Christopher Musco, Ananda Theertha Suresh
Suppose Alice has a distribution and Bob has a distribution . Alice wants to draw a sample and Bob a sample such that with as high of probabilit…
Faster Spectral Density Estimation and Sparsification in the Nuclear Norm
Yujia Jin, Ishani Karmarkar, Christopher Musco +2
We consider the problem of estimating the spectral density of the normalized adjacency matrix of an -node undirected graph. We provide a randomized algorithm that, with $O(nε^{-…
Faster Linear Systems and Matrix Norm Approximation via Multi-level Sketched Preconditioning
Michał Dereziński, Christopher Musco, Jiaming Yang
We present a new class of preconditioned iterative methods for solving linear systems of the form . Our methods are based on constructing a low-rank Nyström approximation t…
A Simple and Practical Method for Reducing the Disparate Impact of Differential Privacy
Lucas Rosenblatt, Julia Stoyanovich, Christopher Musco
Differentially private (DP) mechanisms have been deployed in a variety of high-impact social settings (perhaps most notably by the U.S. Census). Since all DP mechanisms involve add…
Sampling Methods for Inner Product Sketching
Majid Daliri, Juliana Freire, Christopher Musco +2
Recently, Bessa et al. (PODS 2023) showed that sketches based on coordinated weighted sampling theoretically and empirically outperform popular linear sketching methods like Johnso…