Content tagged with game theory

• [B1] Regret, equilibrium, and learning in games: A guided tour
• [C107] The computational intractability of not worst-responding
• [C105] What preferences can–and cannot–predict in multi-agent online learning
• [J47] A quadratic speedup in finding Nash equilibria of quantum zero-sum games
• [J46] Characterizing the convergence of game dynamics via potentialness
• [J45] On the discrete-time origins of the replicator dynamics: From convergence to instability and chaos
• [C101] Robust equilibria in continuous games: From strategic to dynamic robustness
• [C100] Multi-agent learning under uncertainty: Recurrence vs. concentration
• [C99] Efficient kernelized learning in polyhedral games beyond full information: From Colonel Blotto to congestion games
• [C98] Invariance and concentration properties of gradient-based learning in games
• [C97] The impact of uncertainty on regularized learning in games
• [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
• [C84] The stability of matrix multiplicative weights dynamics in quantum games
• [J39] Survival of dominated strategies under imitation dynamics
• [J38] Learning in nonatomic games, Part I: Finite action spaces and population games
• [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
• [C74] Asymptotic degradation of linear regression estimates with strategic data sources
• [W5] A heuristic for estimating Nash equilibria in first-price auctions with correlated values
• [J33] Robust power management via learning and game design
• [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
• [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
• [C61] Adaptive extra-gradient methods for min-max optimization and games
• [J30] When is selfish routing bad? The price of anarchy in light and heavy traffic
• [C59] No-regret learning and mixed Nash equilibria: They do not mix
• [C55] Gradient-free online learning in continuous games with delayed rewards
• [C54] Finite-time last-iterate convergence for multi-agent learning in games
• [D3] Online optimization and learning in games: Theory and applications
• [J27] Learning in games with continuous action sets and unknown payoff functions
• [W4] Multi-agent online learning with imperfect information
• [W3] Online convex optimization and no-regret learning: Algorithms, guarantees and applications
• [J25] Riemannian game dynamics
• [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
• [J19] Auction-based resource allocation in OpenFlow multi-tenant networks
• [J18] Mixed-strategy learning with continuous action sets
• [C34] The asymptotic behavior of the price of anarchy
• [C33] Countering feedback delays in multi-agent learning
• [C31] Learning with bandit feedback in potential games
• [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
• [S1] GameSeer: visualization for game dynamics
• [J17] Learning in games via reinforcement and regularization
• [J13] Imitation dynamics with payoff shocks
• [J11] Inertial game dynamics and applications to constrained optimization
• [J10] Interference-based pricing for opportunistic multi-carrier cognitive radio systems
• [J9] Energy-aware competitive power allocation for heterogeneous networks under QoS constraints
• [J8] Penalty-regulated dynamics and robust learning procedures in games
• [C20] Cost-efficient power allocation in OFDMA cognitive radio networks
• [C15] Adaptive transmit policies for cost-efficient power allocation in multi-carrier systems
• [C14] Energy-aware competitive link adaptation in small-cell networks
• [J6] Higher-order game dynamics
• [J4] Neutral stability, drift, and the diversification of languages
• [C6] Dynamic power allocation games in parallel multiple access channels
• [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
• [C1] The simplex game: Can selfish users learn to operate efficiently in wireless networks?