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Federated minimax optimization: Improved convergence analyses and algorithms
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 …
many modern machine learning applications, such as GANs. Large-scale edge-based …
A faster decentralized algorithm for nonconvex minimax problems
In this paper, we study the nonconvex-strongly-concave minimax optimization problem on
decentralized setting. The minimax problems are attracting increasing attentions because of …
decentralized setting. The minimax problems are attracting increasing attentions because of …
Decentralized policy gradient descent ascent for safe multi-agent reinforcement learning
This paper deals with distributed reinforcement learning problems with safety constraints. In
particular, we consider that a team of agents cooperate in a shared environment, where …
particular, we consider that a team of agents cooperate in a shared environment, where …
Distributed saddle-point problems under data similarity
We study solution methods for (strongly-) convex-(strongly)-concave Saddle-Point Problems
(SPPs) over networks of two type--master/workers (thus centralized) architectures and mesh …
(SPPs) over networks of two type--master/workers (thus centralized) architectures and mesh …
A decentralized parallel algorithm for training generative adversarial nets
Abstract Generative Adversarial Networks (GANs) are a powerful class of generative models
in the deep learning community. Current practice on large-scale GAN training utilizes large …
in the deep learning community. Current practice on large-scale GAN training utilizes large …
Decentralized local stochastic extra-gradient for variational inequalities
We consider distributed stochastic variational inequalities (VIs) on unbounded domains with
the problem data that is heterogeneous (non-IID) and distributed across many devices. We …
the problem data that is heterogeneous (non-IID) and distributed across many devices. We …
Taming communication and sample complexities in decentralized policy evaluation for cooperative multi-agent reinforcement learning
Cooperative multi-agent reinforcement learning (MARL) has received increasing attention in
recent years and has found many scientific and engineering applications. However, a key …
recent years and has found many scientific and engineering applications. However, a key …
Recent theoretical advances in decentralized distributed convex optimization
In the last few years, the theory of decentralized distributed convex optimization has made
significant progress. The lower bounds on communications rounds and oracle calls have …
significant progress. The lower bounds on communications rounds and oracle calls have …
Decentralized distributed optimization for saddle point problems
We consider distributed convex-concave saddle point problems over arbitrary connected
undirected networks and propose a decentralized distributed algorithm for their solution. The …
undirected networks and propose a decentralized distributed algorithm for their solution. The …
[PDF][PDF] Similarity, compression and local steps: three pillars of efficient communications for distributed variational inequalities
Variational inequalities are a broad and flexible class of problems that includes
minimization, saddle point, and fixed point problems as special cases. Therefore, variational …
minimization, saddle point, and fixed point problems as special cases. Therefore, variational …