Unsupervised domain adaptation (UDA) enables the transfer of models trained on source
domains to unlabeled target domains. However, transferring complex time series models …

The minimax problems arise throughout machine learning applications, ranging from
adversarial training and policy evaluation in reinforcement learning to AUROC …

Anderson acceleration (AA) is a popular algorithm designed to enhance the convergence of
fixed-point iterations. In this paper, we introduce a variant of AA based on a truncated Gram …

Anderson mixing (AM) is a classical method that can accelerate fixed-point iterations by
exploring historical information. Despite the successful application of AM in scientific …

This paper develops a new class of nonlinear acceleration algorithms based on extending
conjugate residual-type procedures from linear to nonlinear equations. The main algorithm …

This work introduces and studies the convergence of a stochastic diffusion-optimistic
learning (DOL) strategy for solving distributed nonconvex (NC) and Polyak–Lojasiewicz (PL) …

Anderson mixing (AM) is a useful method that can accelerate fixed-point iterations by
exploring the information from historical iterations. Despite its numerical success in various …

We propose a data-driven and machine-learning-based approach to compute non-Galerkin
coarse-grid operators in multigrid (MG) methods, addressing the well-known issue of …

We propose a data-driven and machine-learning-based approach to compute non-Galerkin
coarse-grid operators in algebraic multigrid (AMG) methods, addressing the well-known …

Nonlinear acceleration methods are powerful techniques to speed up fixed-point iterations.
However, many acceleration methods require storing a large number of previous iterates …