Content tagged with conference

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• [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
• [C91] The computational complexity of finding second-order stationary points
• [C90] What is the long-run distribution of stochastic gradient descent? A large deviations analysis
• [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
• [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
• [C84] The stability of matrix multiplicative weights dynamics in quantum 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
• [C81] Pick your neighbor: Local Gauss-Southwell rule for fast asynchronous decentralized optimization
• [C80] Learning in games with quantized payoff observations
• [C79] Online convex optimization in wireless networks and beyond: The feedback-performance trade-off
• [C78] AdaGrad avoids saddle points
• [C77] UnderGrad: A universal black-box optimization method with almost dimension-free convergence rate guarantees
• [C76] Nested bandits
• [C75] The dynamics of Riemannian Robbins-Monro algorithms
• [C74] Asymptotic degradation of linear regression estimates with strategic data sources
• [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
• [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
• [C65] The last-iterate convergence rate of optimistic mirror descent in stochastic variational inequalities
• [C64] The limits of min-max optimization algorithms: Convergence to spurious non-critical sets
• [C63] Zeroth-order non-convex learning via hierarchical dual averaging
• [C62] Regret minimization in stochastic non-convex learning via a proximal-gradient approach
• [C61] Adaptive extra-gradient methods for min-max optimization and games
• [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
• [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
• [C53] Quick or cheap? Breaking points in dynamic markets
• [C52] Derivative-free optimization over multi-user MIMO networks
• [C51] Online and stochastic optimization beyond Lipschitz continuity: A Riemannian approach
• [C50] On the convergence of single-call stochastic extra-gradient methods
• [C49] An adaptive mirror-prox algorithm for variational inequalities with singular operators
• [C48] Convergent noisy forward-backward-forward algorithms in non-monotone variational inequalities
• [C47] Gradient-free online resource allocation algorithms for dynamic wireless networks
• [C46] Cautious regret minimization: Online optimization with long-term budget constraints
• [C45] Load-aware provisioning of IoT services on Fog computing platforms
• [C44] Optimistic mirror descent in saddle-point problems: Going the extra (gradient) mile
• [C43] Large-scale network utility maximization: Countering exponential growth with exponentiated gradients
• [C42] A Fog-based framework for IoT service provisioning
• [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
• [C38] Power control with random delays: Robust feedback averaging
• [C37] Distributed asynchronous optimization with unbounded delays: How slow can you go?
• [C36] A resource allocation framework for network slicing
• [C35] Cycles in adversarial regularized learning
• [C34] The asymptotic behavior of the price of anarchy
• [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
• [C30] Least action routing: Identifying the optimal path in a wireless relay network
• [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
• [C25] Interference mitigation via pricing in time-varying cognitive radio systems
• [C24] Distributed learning for resource allocation under uncertainty
• [C23] Online interference mitigation via learning in dynamic IoT environments
• [C22] Online power allocation for opportunistic radio access in dynamic OFDM networks
• [C21] A novel dynamic network architecture model based on stochastic geometry and game theory
• [C20] Cost-efficient power allocation in OFDMA cognitive radio networks
• [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
• [C17] No regrets: Distributed power control under time-varying channels and QoS requirements
• [C16] Distributed optimization in multi-user MIMO systems with imperfect and delayed information
• [C15] Adaptive transmit policies for cost-efficient power allocation in multi-carrier systems
• [C14] Energy-aware competitive link adaptation in small-cell networks
• [C13] Adaptive spectrum management in MIMO-OFDM cognitive radio: An exponential learning approach
• [C12] Entropy-driven optimization dynamics for Gaussian vector multiple access channels
• [C11] Accelerating population-based search heuristics by adaptive resource allocation
• [C10] Riemannian-geometric optimization methods for MIMO multiple access channels
• [C9] Strange bedfellows: Riemann, Gibbs and vector Gaussian multiple access channels
• [C8] Matrix exponential learning: Distributed optimization in MIMO systems
• [C7] Selfish Routing Revisited: Degeneracy, Evolution and Stochastic Fluctuations
• [C6] Dynamic power allocation games in parallel multiple access channels
• [C5] Distribution of MIMO mutual information: A large deviations approach
• [C4] Learning in the presence of noise
• [C3] Vertical handover between wireless standards
• [C2] Vertical handover between wireless service providers
• [C1] The simplex game: Can selfish users learn to operate efficiently in wireless networks?