regret bound

Tight Bounds for Bandit Combinatorial Optimization COLT 2017 Unifying PAC and Regret: Uniform PAC Bounds for Episodic Reinforcement Learning NeurIPS 2017 Unifying Task Specification in Reinforcement Learning ICML 2017 Variants of RMSProp and Adagrad with Logarithmic Regret Bounds ICML 2017 Why is Posterior Sampling Better than Optimism for Reinforcement Learning? ICML 2017 ZigZag: A New Approach to Adaptive Online Learning COLT 2017 Accelerating Online Convex Optimization via Adaptive Prediction AISTATS 2016 Adaptive Algorithms for Online Convex Optimization with Long-term Constraints ICML 2016 Adaptive Smoothed Online Multi-Task Learning NeurIPS 2016 Analysis of Classification-based Policy Iteration Algorithms JMLR 2016 An efficient algorithm for contextual bandits with knapsacks, and an extension to concave objectives COLT 2016 An Information-Theoretic Analysis of Thompson Sampling JMLR 2016 Anytime optimal algorithms in stochastic multi-armed bandits ICML 2016 (Bandit) Convex Optimization with Biased Noisy Gradient Oracles AISTATS 2016 Barron and Cover’s Theory in Supervised Learning and its Application to Lasso ICML 2016 Best-of-K-bandits COLT 2016 BISTRO: An Efficient Relaxation-Based Method for Contextual Bandits ICML 2016 Causal Bandits: Learning Good Interventions via Causal Inference NeurIPS 2016 Coin Betting and Parameter-Free Online Learning NeurIPS 2016 Combinatorial Multi-Armed Bandit and Its Extension to Probabilistically Triggered Arms JMLR 2016 Combinatorial Multi-Armed Bandit with General Reward Functions NeurIPS 2016 Combining Adversarial Guarantees and Stochastic Fast Rates in Online Learning NeurIPS 2016 Conservative Bandits ICML 2016 Contextual semibandits via supervised learning oracles NeurIPS 2016 Copeland Dueling Bandit Problem: Regret Lower Bound, Optimal Algorithm, and Computationally Efficient Algorithm ICML 2016