Recent works have shown that stochastic gradient descent (SGD) achieves the fast
convergence rates of full-batch gradient descent for over-parameterized models satisfying …

Convergent sequences of real numbers play a fundamental role in many different problems
in system theory, eg, in Lyapunov stability analysis, as well as in optimization theory and …

Uncertainty Quantification (UQ) is an emerging and extremely active research discipline
which aims to quantitatively treat any uncertainty in applied models. The primary objective of …

Online gradient descent (OGD) is well-known to be doubly optimal under strong convexity or
monotonicity assumptions:(1) in the single-agent setting, it achieves an optimal regret of Θ …

In this paper, we propose a general algorithmic framework for the first-order methods in
optimization in a broad sense, including minimization problems, saddle-point problems and …

In this paper, we consider stochastic monotone Nash games where each player's strategy
set is characterized by possibly a large number of explicit convex constraint inequalities …

We consider the stochastic generalized Nash equilibrium problem (SGNEP) with expected-
value cost functions. Inspired by Yi and Pavel (2019), we propose a distributed generalized …

We examine a flexible algorithmic framework for solving monotone variational inequalities in
the presence of randomness and uncertainty. The proposed template encompasses a wide …

In this work, we conduct the first systematic study of stochastic variational inequality (SVI)
and stochastic saddle point (SSP) problems under the constraint of differential privacy (DP) …

In this paper, we propose a variable sample-size optimistic mirror descent algorithm under
the Bregman distance for a class of stochastic mixed variational inequalities. Different from …