12 citations · 26 across the 6 of their papers we have counts for
9 papers · 1 filter
On Optimal Interpolation In Linear Regression
Eduard Oravkin, Patrick Rebeschini
Understanding when and why interpolating methods generalize well has recently been a topic of interest in statistical learning theory. However, systematically connecting interpolat…
Implicit Regularization in Matrix Sensing via Mirror Descent
Fan Wu, Patrick Rebeschini
We study discrete-time mirror descent applied to the unregularized empirical risk in matrix sensing. In both the general case of rectangular matrices and the particular case of pos…
A Continuous-Time Mirror Descent Approach to Sparse Phase Retrieval
Fan Wu, Patrick Rebeschini
We analyze continuous-time mirror descent applied to sparse phase retrieval, which is the problem of recovering sparse signals from a set of magnitude-only measurements. We apply m…
Decentralised Learning with Random Features and Distributed Gradient Descent
Dominic Richards, Patrick Rebeschini, Lorenzo Rosasco
We investigate the generalisation performance of Distributed Gradient Descent with Implicit Regularisation and Random Features in the homogenous setting where a network of agents a…
Hadamard Wirtinger Flow for Sparse Phase Retrieval
Fan Wu, Patrick Rebeschini
We consider the problem of reconstructing an -dimensional -sparse signal from a set of noiseless magnitude-only measurements. Formulating the problem as an unregularized empi…
The Statistical Complexity of Early-Stopped Mirror Descent
Tomas Vaškevičius, Varun Kanade, Patrick Rebeschini
Recently there has been a surge of interest in understanding implicit regularization properties of iterative gradient-based optimization algorithms. In this paper, we study the sta…