exploration-exploitation tradeoff

Pareto Optimal Model Selection in Linear Bandits AISTATS 2022 Syndicated Bandits: A Framework for Auto Tuning Hyper-parameters in Contextual Bandit Algorithms NeurIPS 2022 Value Function Approximations via Kernel Embeddings for No-Regret Reinforcement Learning ACML 2022 A Provably Efficient Sample Collection Strategy for Reinforcement Learning NeurIPS 2021 Confidence-Budget Matching for Sequential Budgeted Learning ICML 2021 Convex Regularization in Monte-Carlo Tree Search ICML 2021 Deciding What to Learn: A Rate-Distortion Approach ICML 2021 Incentivized Bandit Learning with Self-Reinforcing User Preferences ICML 2021 Instance-Wise Minimax-Optimal Algorithms for Logistic Bandits AISTATS 2021 Kernel-Based Reinforcement Learning: A Finite-Time Analysis ICML 2021 Meta-Learning Effective Exploration Strategies for Contextual Bandits AAAI 2021 Multiplayer Bandit Learning, from Competition to Cooperation COLT 2021 Regulating Greed Over Time in Multi-Armed Bandits JMLR 2021 Reward Biased Maximum Likelihood Estimation for Reinforcement Learning L4DC 2021 Stochastic Linear Bandits Robust to Adversarial Attacks AISTATS 2021 Stochastic Top-K Subset Bandits with Linear Space and Non-Linear Feedback ALT 2021 SUNRISE: A Simple Unified Framework for Ensemble Learning in Deep Reinforcement Learning ICML 2021 VariBAD: Variational Bayes-Adaptive Deep RL via Meta-Learning JMLR 2021 Adaptive Quantitative Trading: An Imitative Deep Reinforcement Learning Approach AAAI 2020 Bayesian model predictive control: Efficient model exploration and regret bounds using posterior sampling L4DC 2020 Conservative Exploration in Reinforcement Learning AISTATS 2020 Fiduciary Bandits ICML 2020 Generative Exploration and Exploitation AAAI 2020 Making Sense of Reinforcement Learning and Probabilistic Inference ICLR 2020 Old Dog Learns New Tricks: Randomized UCB for Bandit Problems AISTATS 2020