Content tagged with 2022

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• [W8] On the rate of convergence of Bregman proximal methods in constrained variational inequalities
• [W7] Explicit second-order min-max optimization methods with optimal convergence guarantees
• [W6] A unified stochastic approximation framework for learning in games
• [J40] Multi-agent online learning in time-varying games
• [J39] Survival of dominated strategies under imitation dynamics
• [J38] Learning in nonatomic games, Part I: Finite action spaces and population games
• [J37] Distributed stochastic optimization with large delays
• [J36] Multi-agent online optimization with delays: Asynchronicity, adaptivity, and optimism
• [J35] Online reconfiguration of IoT applications in the Fog: The information-coordination trade-off
• [C83] On the convergence of policy gradient methods to Nash equilibria in general stochastic games
• [C82] No-regret learning in games with noisy feedback: Faster rates and adaptivity via learning rate separation
• [C81] Pick your neighbor: Local Gauss-Southwell rule for fast asynchronous decentralized optimization
• [C80] Learning in games with quantized payoff observations
• [C79] Online convex optimization in wireless networks and beyond: The feedback -performance trade-off
• [C78] AdaGrad avoids saddle points
• [C77] UnderGrad: A universal black-box optimization method with almost dimension-free convergence rate guarantees
• [C76] Nested bandits
• [C75] The dynamics of Riemannian Robbins-Monro algorithms
• [C74] Asymptotic degradation of linear regression estimates with strategic data sources