Stochastic nested optimization, including stochastic compositional, min-max, and bilevel
optimization, is gaining popularity in many machine learning applications. While the three …

Byzantine robustness has received significant attention recently given its importance for
distributed and federated learning. In spite of this, we identify severe flaws in existing …

Standard federated optimization methods successfully apply to stochastic problems with
single-level structure. However, many contemporary ML problems-including adversarial …

Gradient descent ascent (GDA), the simplest single-loop algorithm for nonconvex minimax
optimization, is widely used in practical applications such as generative adversarial …

In this paper, we consider nonconvex minimax optimization, which is gaining prominence in
many modern machine learning applications, such as GANs. Large-scale edge-based …

In this paper, we study the nonconvex-strongly-concave minimax optimization problem on
decentralized setting. The minimax problems are attracting increasing attentions because of …

Two of the most prominent algorithms for solving unconstrained smooth games are the
classical stochastic gradient descent-ascent (SGDA) and the recently introduced stochastic …

Single-call stochastic extragradient methods, like stochastic past extragradient (SPEG) and
stochastic optimistic gradient (SOG), have gained a lot of interest in recent years and are …

In the paper, we propose a class of accelerated zeroth-order and first-order momentum
methods for both nonconvex mini-optimization and minimax-optimization. Specifically, we …

We investigate safe multi-agent reinforcement learning, where agents seek to collectively
maximize an aggregate sum of local objectives while satisfying their own safety constraints …