Content tagged with online learning

• [W9] On the discrete-time origins of the replicator dynamics: From convergence to instability and chaos
• [J44] Nested replicator dynamics, nested logit choice, and similarity-based learning
• [J41] A unified stochastic approximation framework for learning in games
• [C94] Accelerated regularized learning in finite $N$-person games
• [C93] No-regret learning in harmonic games: Extrapolation in the presence of conflicting interests
• [C92] A geometric decomposition of finite games: Convergence vs. recurrence under exponential weights
• [W6] Learning in quantum games
• [J40] Multi-agent online learning in time-varying games
• [C89] A quadratic speedup in finding Nash equilibria of quantum zero-sum 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
• [C85] Exploiting hidden structures in non-convex games for convergence to Nash equilibrium
• [J38] Learning in nonatomic games, Part I: Finite action spaces and population games
• [J36] Multi-agent online optimization with delays: Asynchronicity, adaptivity, and optimism
• [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
• [C80] Learning in games with quantized payoff observations
• [C79] Online convex optimization in wireless networks and beyond: The feedback-performance trade-off
• [C76] Nested bandits
• [W5] A heuristic for estimating Nash equilibria in first-price auctions with correlated values
• [C72] The convergence rate of regularized learning in games: From bandits and uncertainty to optimism and beyond
• [C69] Equilibrium tracking and convergence in dynamic games
• [C68] Optimization in open networks via dual averaging
• [C67] Adaptive learning in continuous games: Optimal regret bounds and convergence to Nash equilibrium
• [C66] Survival of the strictest: Stable and unstable equilibria under regularized learning with partial information
• [C63] Zeroth-order non-convex learning via hierarchical dual averaging
• [C62] Regret minimization in stochastic non-convex learning via a proximal-gradient approach
• [C59] No-regret learning and mixed Nash equilibria: They do not mix
• [C58] Online non-convex optimization with imperfect feedback
• [C56] A new regret analysis for Adam-type algorithms
• [C55] Gradient-free online learning in continuous games with delayed rewards
• [C54] Finite-time last-iterate convergence for multi-agent learning in games
• [C52] Derivative-free optimization over multi-user MIMO networks
• [C51] Online and stochastic optimization beyond Lipschitz continuity: A Riemannian approach
• [D3] Online optimization and learning in games: Theory and applications
• [J28] Online power optimization in feedback-limited, dynamic and unpredictable IoT networks
• [J27] Learning in games with continuous action sets and unknown payoff functions
• [C47] Gradient-free online resource allocation algorithms for dynamic wireless networks
• [C46] Cautious regret minimization: Online optimization with long-term budget constraints
• [W4] Multi-agent online learning with imperfect information
• [W3] Online convex optimization and no-regret learning: Algorithms, guarantees and applications
• [C41] Bandit learning in concave N-person games
• [C40] Learning in games with lossy feedback
• [C35] Cycles in adversarial regularized learning
• [J23] On the robustness of learning in games with stochastically perturbed payoff observations
• [J21] A continuous-time approach to online optimization
• [J18] Mixed-strategy learning with continuous action sets
• [C33] Countering feedback delays in multi-agent learning
• [C31] Learning with bandit feedback in potential games
• [C29] Power control in wireless networks via dual averaging
• [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
• [J17] Learning in games via reinforcement and regularization
• [J14] Learning to be green: Robust energy efficiency maximization in dynamic MIMO-OFDM systems
• [J12] Learning in an uncertain world: MIMO covariance matrix optimization with imperfect feedback
• [C25] Interference mitigation via pricing in time-varying cognitive radio systems
• [C23] Online interference mitigation via learning in dynamic IoT environments
• [C22] Online power allocation for opportunistic radio access in dynamic OFDM networks
• [W1] Power control via online learning in non-stationary MIMO networks
• [J8] Penalty-regulated dynamics and robust learning procedures in games
• [C19] No more tears: A no-regret approach to power control in dynamically varying MIMO networks
• [C18] Energy-efficient power allocation in dynamic multi-carrier systems
• [J7] Transmit without regrets: Online optimization in MIMO–OFDM cognitive radio systems
• [C17] No regrets: Distributed power control under time-varying channels and QoS requirements
• [J6] Higher-order game dynamics
• [C13] Adaptive spectrum management in MIMO-OFDM cognitive radio: An exponential learning approach
• [C11] Accelerating population-based search heuristics by adaptive resource allocation
• [J2] The emergence of rational behavior in the presence of stochastic perturbations
• [C4] Learning in the presence of noise