regret bound

Incentivized Bandit Learning with Self-Reinforcing User Preferences ICML 2021 Indexed Minimum Empirical Divergence for Unimodal Bandits NeurIPS 2021 Instance-Wise Minimax-Optimal Algorithms for Logistic Bandits AISTATS 2021 Is Reinforcement Learning More Difficult Than Bandits? A Near-optimal Algorithm Escaping the Curse of Horizon COLT 2021 Jointly Learning Prices and Product Features IJCAI 2021 Joint Online Learning and Decision-making via Dual Mirror Descent ICML 2021 Kernel-Based Reinforcement Learning: A Finite-Time Analysis ICML 2021 Lazy OCO: Online Convex Optimization on a Switching Budget COLT 2021 Learning Equilibria in Matching Markets from Bandit Feedback NeurIPS 2021 Learning Infinite-horizon Average-reward MDPs with Linear Function Approximation AISTATS 2021 Learning in Multi-Player Stochastic Games UAI 2021 Learning in Non-Cooperative Configurable Markov Decision Processes NeurIPS 2021 Learning Policies with Zero or Bounded Constraint Violation for Constrained MDPs NeurIPS 2021 Learning Stabilizing Controllers for Unstable Linear Quadratic Regulators from a Single Trajectory L4DC 2021 Learning-to-learn non-convex piecewise-Lipschitz functions NeurIPS 2021 Lenient Regret and Good-Action Identification in Gaussian Process Bandits ICML 2021 Lenient Regret for Multi-Armed Bandits AAAI 2021 Leveraging Good Representations in Linear Contextual Bandits ICML 2021 Lifelong Learning with Branching Experts ACML 2021 Littlestone Classes are Privately Online Learnable NeurIPS 2021 Local Differential Privacy for Bayesian Optimization AAAI 2021 Logarithmic Regret for Reinforcement Learning with Linear Function Approximation ICML 2021 Logarithmic Regret from Sublinear Hints NeurIPS 2021 Logarithmic Regret in Feature-based Dynamic Pricing NeurIPS 2021 Low-Rank Generalized Linear Bandit Problems AISTATS 2021