Online Algorithms

Scale-free Unconstrained Online Learning for Curved Losses COLT 2022 Simultaneously Learning Stochastic and Adversarial Bandits with General Graph Feedback ICML 2022 Smoothed Adversarial Linear Contextual Bandits with Knapsacks ICML 2022 Stochastic Online Learning with Feedback Graphs: Finite-Time and Asymptotic Optimality NeurIPS 2022 Stochastic Rising Bandits ICML 2022 The Evolution of Uncertainty of Learning in Games ICLR 2022 The Pareto Frontier of Instance-Dependent Guarantees in Multi-Player Multi-Armed Bandits with no Communication COLT 2022 The price of unfairness in linear bandits with biased feedback NeurIPS 2022 Thompson Sampling Achieves Õ(√T) Regret in Linear Quadratic Control COLT 2022 Top K Ranking for Multi-Armed Bandit with Noisy Evaluations AISTATS 2022 Trading Off Resource Budgets For Improved Regret Bounds NeurIPS 2022 Triangle and Four Cycle Counting with Predictions in Graph Streams ICLR 2022 Universal and data-adaptive algorithms for model selection in linear contextual bandits ICML 2022 Universal Online Learning: an Optimistically Universal Learning Rule COLT 2022 Versatile Dueling Bandits: Best-of-both World Analyses for Learning from Relative Preferences ICML 2022 ACAT-G: An Interactive Learning Framework for Assisted Response Generation AAAI 2021 A Contextual Bandit Bake-off JMLR 2021 Active Online Learning with Hidden Shifting Domains AISTATS 2021 Adaptive Approximate Policy Iteration AISTATS 2021 Adaptive Discretization for Adversarial Lipschitz Bandits COLT 2021 Adaptive Learning in Continuous Games: Optimal Regret Bounds and Convergence to Nash Equilibrium COLT 2021 Addressing the Long-term Impact of ML Decisions via Policy Regret IJCAI 2021 Adversarial Dueling Bandits ICML 2021 A Gang of Adversarial Bandits NeurIPS 2021 AI for Social Good: Between My Research and the Real World AAAI 2021