In this paper, we investigate the impact of stochasticity and large stepsizes on the implicit
regularisation of gradient descent (GD) and stochastic gradient descent (SGD) over $2 …

We study a variation of vanilla stochastic gradient descent where the optimizer only has
access to a Markovian sampling scheme. These schemes encompass applications that …

In this paper, we investigate the impact of stochasticity and large stepsizes on the implicit
regularisation of gradient descent (GD) and stochastic gradient descent (SGD) over …

Local convexity of the TAP free energy and AMP convergence for Z2-synchronization Page 1
The Annals of Statistics 2023, Vol. 51, No. 2, 519–546 https://doi.org/10.1214/23-AOS2257 © …

We investigate the convergence of stochastic mirror descent (SMD) under interpolation in
relatively smooth and smooth convex optimization. In relatively smooth convex optimization …

We study finite-sum distributed optimization problems involving a master node and $ n-1$
local nodes under the popular $\delta $-similarity and $\mu $-strong convexity conditions …

In personalized federated learning, each member of a potentially large set of agents aims to
train a model minimizing its loss function averaged over its local data distribution. We study …

The widely used stochastic gradient methods for minimizing nonconvex composite objective
functions require the Lipschitz smoothness of the differentiable part. But the requirement …

Motivated by the computation of the non-parametric maximum likelihood estimator (NPMLE)
and the Bayesian posterior in statistics, this paper explores the problem of convex …

We present an algorithm for minimizing an objective with hard-to-compute gradients by
using a related, easier-to-access function as a proxy. Our algorithm is based on approximate …