Online Algorithms

Learning Adversarial Markov Decision Processes with Bandit Feedback and Unknown Transition ICML 2020 Learning Augmented Energy Minimization via Speed Scaling NeurIPS 2020 Learning piecewise Lipschitz functions in changing environments AISTATS 2020 Learning to Optimize Computational Resources: Frugal Training with Generalization Guarantees AAAI 2020 Learning URI Selection Criteria to Improve the Crawling of Linked Open Data (Extended Abstract) IJCAI 2020 Linear Bandits with Feature Feedback AAAI 2020 Lipschitz and Comparator-Norm Adaptivity in Online Learning COLT 2020 Locally-Adaptive Nonparametric Online Learning NeurIPS 2020 (Locally) Differentially Private Combinatorial Semi-Bandits ICML 2020 Making Non-Stochastic Control (Almost) as Easy as Stochastic NeurIPS 2020 MAP Inference for Customized Determinantal Point Processes via Maximum Inner Product Search AISTATS 2020 Minimax Regret of Switching-Constrained Online Convex Optimization: No Phase Transition NeurIPS 2020 Mitigating Manipulation in Peer Review via Randomized Reviewer Assignments NeurIPS 2020 Modelling and Solving Online Optimisation Problems AAAI 2020 Model Selection for Production System via Automated Online Experiments NeurIPS 2020 Multi-Player Bandits: The Adversarial Case JMLR 2020 My Fair Bandit: Distributed Learning of Max-Min Fairness with Multi-player Bandits ICML 2020 Non-Stochastic Control with Bandit Feedback NeurIPS 2020 No-regret Learning in Price Competitions under Consumer Reference Effects NeurIPS 2020 No-Regret Prediction in Marginally Stable Systems COLT 2020 Observe Before Play: Multi-Armed Bandit with Pre-Observations AAAI 2020 On Adaptivity in Information-Constrained Online Learning AAAI 2020 Online Agnostic Boosting via Regret Minimization NeurIPS 2020 Online Algorithm for Unsupervised Sequential Selection with Contextual Information NeurIPS 2020 Online Algorithms for Multi-shop Ski Rental with Machine Learned Advice NeurIPS 2020