Turnitin
降AI改写
早检测系统
早降重系统
Turnitin-UK版
万方检测-期刊版
维普编辑部版
Grammarly检测
Paperpass检测
checkpass检测
PaperYY检测
Closing the gap: Tighter analysis of alternating stochastic gradient methods for bilevel problems
Stochastic nested optimization, including stochastic compositional, min-max, and bilevel
optimization, is gaining popularity in many machine learning applications. While the three …
optimization, is gaining popularity in many machine learning applications. While the three …
Fednest: Federated bilevel, minimax, and compositional optimization
Standard federated optimization methods successfully apply to stochastic problems with
single-level structure. However, many contemporary ML problems-including adversarial …
single-level structure. However, many contemporary ML problems-including adversarial …
Fast extra gradient methods for smooth structured nonconvex-nonconcave minimax problems
Modern minimax problems, such as generative adversarial network and adversarial training,
are often under a nonconvex-nonconcave setting, and develo** an efficient method for …
are often under a nonconvex-nonconcave setting, and develo** an efficient method for …
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 …
The confluence of networks, games, and learning a game-theoretic framework for multiagent decision making over networks
Multiagent decision making over networks has recently attracted an exponentially growing
number of researchers from the systems and control community. The area has gained …
number of researchers from the systems and control community. The area has gained …
Faster single-loop algorithms for minimax optimization without strong concavity
Gradient descent ascent (GDA), the simplest single-loop algorithm for nonconvex minimax
optimization, is widely used in practical applications such as generative adversarial …
optimization, is widely used in practical applications such as generative adversarial …
Stochastic gradient descent-ascent: Unified theory and new efficient methods
Abstract Stochastic Gradient Descent-Ascent (SGDA) is one of the most prominent
algorithms for solving min-max optimization and variational inequalities problems (VIP) …
algorithms for solving min-max optimization and variational inequalities problems (VIP) …
The complexity of nonconvex-strongly-concave minimax optimization
This paper studies the complexity for finding approximate stationary points of nonconvex-
strongly-concave (NC-SC) smooth minimax problems, in both general and averaged smooth …
strongly-concave (NC-SC) smooth minimax problems, in both general and averaged smooth …
Solving a class of non-convex minimax optimization in federated learning
The minimax problems arise throughout machine learning applications, ranging from
adversarial training and policy evaluation in reinforcement learning to AUROC …
adversarial training and policy evaluation in reinforcement learning to AUROC …
Stochastic gradient descent-ascent and consensus optimization for smooth games: Convergence analysis under expected co-coercivity
Two of the most prominent algorithms for solving unconstrained smooth games are the
classical stochastic gradient descent-ascent (SGDA) and the recently introduced stochastic …
classical stochastic gradient descent-ascent (SGDA) and the recently introduced stochastic …