Content tagged with NeurIPS

• [C88] The equivalence of dynamic and strategic stability under regularized learning in games
• [C87] Riemannian stochastic optimization methods avoid strict saddle points
• [C86] Payoff-based learning with matrix multiplicative weights in quantum games
• [C85] Exploiting hidden structures in non-convex games for convergence to Nash equilibrium
• [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
• [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
• [C60] On the almost sure convergence of stochastic gradient descent in non-convex problems
• [C59] No-regret learning and mixed Nash equilibria: They do not mix
• [C58] Online non-convex optimization with imperfect feedback
• [C57] Explore aggressively, update conservatively: Stochastic extragradient methods with variable stepsize scaling
• [C50] On the convergence of single-call stochastic extra-gradient methods
• [C49] An adaptive mirror-prox algorithm for variational inequalities with singular operators
• [C41] Bandit learning in concave N-person games
• [C40] Learning in games with lossy feedback
• [C39] On the convergence of stochastic forward-backward-forward algorithms with variance reduction
• [C33] Countering feedback delays in multi-agent learning
• [C32] Stochastic mirror descent in variationally coherent optimization problems
• [C31] Learning with bandit feedback in potential games