93 citations · 388 across the 44 of their papers we have counts for
5 papers · 1 filter
Universal Matrix Sparsifiers and Fast Deterministic Algorithms for Linear Algebra
Rajarshi Bhattacharjee, Gregory Dexter, Cameron Musco +3
Let satisfy , where is the all ones matrix and is the spectral norm. It is well-kno…
On the Unreasonable Effectiveness of Single Vector Krylov Methods for Low-Rank Approximation
Raphael A. Meyer, Cameron Musco, Christopher Musco
Krylov subspace methods are a ubiquitous tool for computing near-optimal rank approximations of large matrices. While "large block" Krylov methods with block size at least …
No-regret Algorithms for Fair Resource Allocation
Abhishek Sinha, Ativ Joshi, Rajarshi Bhattacharjee +2
We consider a fair resource allocation problem in the no-regret setting against an unrestricted adversary. The objective is to allocate resources equitably among several agents in…
Nearly Optimal Approximation of Matrix Functions by the Lanczos Method
Noah Amsel, Tyler Chen, Anne Greenbaum +2
Approximating the action of a matrix function on a vector is an increasingly important primitive in machine learning, data science, and statistics, wit…
Weighted Minwise Hashing Beats Linear Sketching for Inner Product Estimation
Aline Bessa, Majid Daliri, Juliana Freire +4
We present a new approach for computing compact sketches that can be used to approximate the inner product between pairs of high-dimensional vectors. Based on the Weighted MinHash…