93 citations · 388 across the 44 of their papers we have counts for
6 papers · 1 filter
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…
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…
Active Linear Regression for Norms and Beyond
Cameron Musco, Christopher Musco, David P. Woodruff +1
We study active sampling algorithms for linear regression, which aim to query only a few entries of a target vector and output a near minimizer to $\min_{x\in\mat…
Estimation of Shortest Path Covariance Matrices
Raj Kumar Maity, Cameron Musco
We study the sample complexity of estimating the covariance matrix of a distribution over given independent sample…
Random Fourier Features for Kernel Ridge Regression: Approximation Bounds and Statistical Guarantees
Haim Avron, Michael Kapralov, Cameron Musco +3
Random Fourier features is one of the most popular techniques for scaling up kernel methods, such as kernel ridge regression. However, despite impressive empirical results, the sta…
Recursive Sampling for the Nyström Method
Cameron Musco, Christopher Musco
We give the first algorithm for kernel Nyström approximation that runs in *linear time in the number of training points* and is provably accurate for all kernel matrices, without d…