11 papers
Lonic: Algorithm-Hardware Co-Design for Energy-Efficient Fully Local Online SNN Training with INT4 Precision
Peilin Chen, Xiaoxuan Yang
Spiking neural networks (SNNs) have recently attracted increasing attention as an energy-efficient learning paradigm. Existing works also propose temporally and fully local online…
Sublinear Time Eigenvector Approximation via Column Sampling
Rajarshi Bhattacharjee, Cameron Musco, Dominic Rutkowski
We study sublinear time sampling methods for approximating the outlying eigenvectors of large matrices. Our main result is an algorithm that uniformly samples just $\tilde{O}(\log…
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…
Private Adaptive Covariance Estimation via Gaussian Graphical Models
Cecilia Ferrando, Miguel Fuentes, Brett Mullins +2
We propose PACE-GGM, a data-adaptive differentially private method for covariance estimation that concentrates its privacy budget on the most informative entries of the empirical c…
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…
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…