93 citations · 388 across the 44 of their papers we have counts for
5 papers · 1 filter
Sublinear Time Low-Rank Approximation of Hankel Matrices
Michael Kapralov, Cameron Musco, Kshiteej Sheth
Hankel matrices are an important class of highly-structured matrices, arising across computational mathematics, engineering, and theoretical computer science. It is well-known that…
A Note on Fine-Grained Quantum Reductions for Linear Algebraic Problems
Kyle Doney, Cameron Musco
We observe that any time algorithm (quantum or classical) for several central linear algebraic problems, such as computing , , or for an $n \t…
Query Efficient Structured Matrix Learning
Noah Amsel, Pratyush Avi, Tyler Chen +5
We study the problem of learning a structured approximation (low-rank, sparse, banded, etc.) to an unknown matrix given access to matrix-vector product (matvec) queries of the…
Distance Adaptive Beam Search for Provably Accurate Graph-Based Nearest Neighbor Search
Yousef Al-Jazzazi, Haya Diwan, Jinrui Gou +3
Nearest neighbor search is central in machine learning, information retrieval, and databases. For high-dimensional datasets, graph-based methods such as HNSW, DiskANN, and NSG have…
Quasi-optimal hierarchically semi-separable matrix approximation
Noah Amsel, Tyler Chen, Feyza Duman Keles +4
We present a randomized algorithm for producing a quasi-optimal hierarchically semi-separable (HSS) approximation to an matrix using only matrix-vector products wit…