Content tagged with stochastic methods

• [W10] Tamed Langevin sampling under weaker conditions
• [J42] Quick or cheap? Breaking points in dynamic markets
• [J41] A unified stochastic approximation framework for learning in games
• [C94] Accelerated regularized learning in finite $N$-person games
• [C90] What is the long-run distribution of stochastic gradient descent? A large deviations analysis
• [W7] Setwise coordinate descent for dual asynchronous decentralized optimization
• [J40] Multi-agent online learning in time-varying games
• [C88] The equivalence of dynamic and strategic stability under regularized learning in games
• [C87] Riemannian stochastic optimization methods avoid strict saddle points
• [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
• [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
• [J34] Minibatch forward-backward-forward methods for solving stochastic variational inequalities
• [C73] Fast routing under uncertainty: Adaptive learning in congestion games with exponential weights
• [C72] The convergence rate of regularized learning in games: From bandits and uncertainty to optimism and beyond
• [C71] Sifting through the noise: Universal first-order methods for stochastic variational inequalities
• [C70] Adaptive first-order methods revisited: Convex optimization without Lipschitz requirements
• [C66] Survival of the strictest: Stable and unstable equilibria under regularized learning with partial information
• [C65] The last-iterate convergence rate of optimistic mirror descent in stochastic variational inequalities
• [C64] The limits of min-max optimization algorithms: Convergence to spurious non-critical sets
• [C63] Zeroth-order non-convex learning via hierarchical dual averaging
• [C62] Regret minimization in stochastic non-convex learning via a proximal-gradient approach
• [J31] On the convergence of mirror descent beyond stochastic convex programming
• [C60] On the almost sure convergence of stochastic gradient descent in non-convex problems
• [C58] Online non-convex optimization with imperfect feedback
• [C57] Explore aggressively, update conservatively: Stochastic extragradient methods with variable stepsize scaling
• [C56] A new regret analysis for Adam-type algorithms
• [C54] Finite-time last-iterate convergence for multi-agent learning in games
• [C53] Quick or cheap? Breaking points in dynamic markets
• [C51] Online and stochastic optimization beyond Lipschitz continuity: A Riemannian approach
• [J27] Learning in games with continuous action sets and unknown payoff functions
• [C50] On the convergence of single-call stochastic extra-gradient methods
• [C48] Convergent noisy forward-backward-forward algorithms in non-monotone variational inequalities
• [C44] Optimistic mirror descent in saddle-point problems: Going the extra (gradient) mile
• [J26] Stochastic mirror descent dynamics and their convergence in monotone variational inequalities
• [J24] On the convergence of gradient-like flows with noisy gradient input
• [C39] On the convergence of stochastic forward-backward-forward algorithms with variance reduction
• [C37] Distributed asynchronous optimization with unbounded delays: How slow can you go?
• [J23] On the robustness of learning in games with stochastically perturbed payoff observations
• [J22] Distributed stochastic optimization via matrix exponential learning
• [J21] A continuous-time approach to online optimization
• [C32] Stochastic mirror descent in variationally coherent optimization problems
• [C28] Mirror descent learning in continuous games
• [C27] Convergence to Nash equilibrium in continuous games with noisy first-order feedback
• [C26] Hedging under uncertainty: regret minimization meets exponentially fast convergence
• [J15] A stochastic approximation algorithm for stochastic semidefinite programming
• [J13] Imitation dynamics with payoff shocks
• [C24] Distributed learning for resource allocation under uncertainty
• [J8] Penalty-regulated dynamics and robust learning procedures in games
• [C7] Selfish Routing Revisited: Degeneracy, Evolution and Stochastic Fluctuations
• [J2] The emergence of rational behavior in the presence of stochastic perturbations
• [D2] Stochastic perturbations in game theory and applications to networks
• [C4] Learning in the presence of noise