Content tagged with bandit feedback

• [J41] A unified stochastic approximation framework for learning in games
• [J40] Multi-agent online learning in time-varying games
• [C88] The equivalence of dynamic and strategic stability under regularized learning in games
• [C86] Payoff-based learning with matrix multiplicative weights in quantum games
• [C80] Learning in games with quantized payoff observations
• [C76] Nested bandits
• [C63] Zeroth-order non-convex learning via hierarchical dual averaging
• [J32] Fast optimization with zeroth-order feedback in distributed multi-user MIMO systems
• [C58] Online non-convex optimization with imperfect feedback
• [C55] Gradient-free online learning in continuous games with delayed rewards
• [C47] Gradient-free online resource allocation algorithms for dynamic wireless networks
• [C41] Bandit learning in concave N-person games
• [C31] Learning with bandit feedback in potential games