NeurIPS

Content tagged with NeurIPS

[C94] Accelerated regularized learning in finite $N$-person games
[C93] No-regret learning in harmonic games: Extrapolation in the presence of conflicting interests
[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