upper confidence bound

Adaptive Sampling for Estimating Distributions: A Bayesian Upper Confidence Bound Approach L4DC 2021 (Almost) Free Incentivized Exploration from Decentralized Learning Agents NeurIPS 2021 Asymptotically Optimal Information-Directed Sampling COLT 2021 Bandits with many optimal arms NeurIPS 2021 Combinatorial Blocking Bandits with Stochastic Delays ICML 2021 Combinatorial Gaussian Process Bandits with Probabilistically Triggered Arms AISTATS 2021 Contextual Blocking Bandits AISTATS 2021 DDSAS: Dynamic and Differentiable Space-Architecture Search ACML 2021 Decentralized Multi-Agent Linear Bandits with Safety Constraints AAAI 2021 Double Explore-then-Commit: Asymptotic Optimality and Beyond COLT 2021 Dynamic Bottleneck for Robust Self-Supervised Exploration NeurIPS 2021 Dynamic Planning and Learning under Recovering Rewards ICML 2021 DynaTune: Dynamic Tensor Program Optimization in Deep Neural Network Compilation ICLR 2021 Efficient Algorithms for Stochastic Repeated Second-price Auctions ALT 2021 Exploiting Opponents Under Utility Constraints in Sequential Games NeurIPS 2021 Federated Multi-Armed Bandits AAAI 2021 Federated Multi-armed Bandits with Personalization AISTATS 2021 Fine-Grained Gap-Dependent Bounds for Tabular MDPs via Adaptive Multi-Step Bootstrap COLT 2021 Learning to Plan Optimistically: Uncertainty-Guided Deep Exploration via Latent Model Ensembles CoRL 2021 MADE: Exploration via Maximizing Deviation from Explored Regions NeurIPS 2021 Multi-Armed Bandits with Cost Subsidy AISTATS 2021 Multinomial Logit Contextual Bandits: Provable Optimality and Practicality AAAI 2021 Multitask Bandit Learning Through Heterogeneous Feedback Aggregation AISTATS 2021 Near-Optimal Model-Free Reinforcement Learning in Non-Stationary Episodic MDPs ICML 2021 No-regret Algorithms for Multi-task Bayesian Optimization AISTATS 2021