93 citations · 388 across the 44 of their papers we have counts for
6 papers · 1 filter
Improved Spectral Density Estimation via Explicit and Implicit Deflation
Rajarshi Bhattacharjee, Rajesh Jayaram, Cameron Musco +2
We study algorithms for approximating the spectral density of a symmetric matrix that is accessed through matrix-vector product queries. By combining a previously studied Cheby…
Sharper Bounds for Chebyshev Moment Matching, with Applications
Cameron Musco, Christopher Musco, Lucas Rosenblatt +1
We study the problem of approximately recovering a probability distribution given noisy measurements of its Chebyshev polynomial moments. This problem arises broadly across algorit…
Near-optimal hierarchical matrix approximation from matrix-vector products
Tyler Chen, Feyza Duman Keles, Diana Halikias +3
We describe a randomized algorithm for producing a near-optimal hierarchical off-diagonal low-rank (HODLR) approximation to an matrix , accessible only thou…
Navigable Graphs for High-Dimensional Nearest Neighbor Search: Constructions and Limits
Haya Diwan, Jinrui Gou, Cameron Musco +2
There has been significant recent interest in graph-based nearest neighbor search methods, many of which are centered on the construction of navigable graphs over high-dimensional…
Sublinear Time Low-Rank Approximation of Toeplitz Matrices
Cameron Musco, Kshiteej Sheth
We present a sublinear time algorithm for computing a near optimal low-rank approximation to any positive semidefinite (PSD) Toeplitz matrix , given no…
Fixed-sparsity matrix approximation from matrix-vector products
Noah Amsel, Tyler Chen, Feyza Duman Keles +3
We study the problem of approximating a matrix with a matrix that has a fixed sparsity pattern (e.g., diagonal, banded, etc.), when is accessed only by ma…