Abstract Machine unlearning is an emerging task of removing the influence of selected
training datapoints from a trained model upon data deletion requests, which echoes the …

In this paper, we propose a cubic regularized Newton method for solving the convex-
concave minimax saddle point problems. At each iteration, a cubic regularized saddle point …

The success of minimax learning problems of generative adversarial networks (GANs) has
been observed to depend on the minimax optimization algorithm used for their training. This …

We study the problem of $(\epsilon,\delta) $-certified machine unlearning for minimax
models. Most of the existing works focus on unlearning from standard statistical learning …

We study the bilinearly coupled minimax problem: $\min_ {x}\max_ {y} f (x)+ y^\top A xh (y) $,
where $ f $ and $ h $ are both strongly convex smooth functions and admit first-order …

We consider a distributed consensus optimization problem over a server-client (federated)
network, where all clients are connected to a central server. Current distributed algorithms …

We study gradient descent-ascent learning dynamics with timescale separation ($\tau $-
GDA) in unconstrained continuous action zero-sum games where the minimizing player …

We study the role that a finite timescale separation parameter τ has on gradient descent-
ascent in non-convex, non-concave zero-sum games where the learning rate of player 1 is …

We provide improved convergence rates for constrained convex-concave min-max problems
and monotone variational inequalities with higher-order smoothness. In min-max settings …

Matrix factorization (MF) is a fundamental model in data mining and machine learning, which
finds wide applications in diverse application areas, including recommendation systems with …