Online Algorithms

Lenient Regret for Multi-Armed Bandits AAAI 2021 Leveraging Good Representations in Linear Contextual Bandits ICML 2021 Linear Bandits on Uniformly Convex Sets JMLR 2021 Logarithmic Regret from Sublinear Hints NeurIPS 2021 Logarithmic Regret in Feature-based Dynamic Pricing NeurIPS 2021 Majorizing Measures, Sequential Complexities, and Online Learning COLT 2021 Making the most of your day: online learning for optimal allocation of time NeurIPS 2021 Margin-Independent Online Multiclass Learning via Convex Geometry NeurIPS 2021 Matchings with Group Fairness Constraints: Online and Offline Algorithms IJCAI 2021 Matrix games with bandit feedback UAI 2021 Max-Utility Based Arm Selection Strategy For Sequential Query Recommendations ACML 2021 MetaGrad: Adaptation using Multiple Learning Rates in Online Learning JMLR 2021 Minimax Optimal Quantile and Semi-Adversarial Regret via Root-Logarithmic Regularizers NeurIPS 2021 Minimax Regret for Stochastic Shortest Path with Adversarial Costs and Known Transition COLT 2021 Mirror Descent and the Information Ratio COLT 2021 Mixability made efficient: Fast online multiclass logistic regression NeurIPS 2021 Multi-armed Bandit Requiring Monotone Arm Sequences NeurIPS 2021 Multi-Armed Bandits with Bounded Arm-Memory: Near-Optimal Guarantees for Best-Arm Identification and Regret Minimization NeurIPS 2021 Multi-Goal Multi-Agent Path Finding via Decoupled and Integrated Goal Vertex Ordering AAAI 2021 Multi-layered Network Exploration via Random Walks: From Offline Optimization to Online Learning ICML 2021 Multinomial Logit Contextual Bandits: Provable Optimality and Practicality AAAI 2021 Multi-Receiver Online Bayesian Persuasion ICML 2021 Multitask Bandit Learning Through Heterogeneous Feedback Aggregation AISTATS 2021 Near-Optimal Confidence Sequences for Bounded Random Variables ICML 2021 Near-Optimal MNL Bandits Under Risk Criteria AAAI 2021