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cs.DS2025
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…
cs.DS2025
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…
cs.DS2025
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…