Online Algorithms

A Unified Framework of Online Learning Algorithms for Training Recurrent Neural Networks JMLR 2020 A Unified Model for the Two-stage Offline-then-Online Resource Allocation IJCAI 2020 Balancing the Tradeoff between Profit and Fairness in Rideshare Platforms during High-Demand Hours AAAI 2020 Bandit Convex Optimization in Non-stationary Environments AISTATS 2020 Bandit Linear Control NeurIPS 2020 Bandits with Adversarial Scaling ICML 2020 Better Full-Matrix Regret via Parameter-Free Online Learning NeurIPS 2020 Bisection-Based Pricing for Repeated Contextual Auctions against Strategic Buyer ICML 2020 Bounded Rationality in Las Vegas: Probabilistic Finite Automata Play Multi-Armed Bandits UAI 2020 Bounding Regret in Empirical Games AAAI 2020 Budget-Constrained Bandits over General Cost and Reward Distributions AISTATS 2020 Budgeted Online Influence Maximization ICML 2020 Choice Bandits NeurIPS 2020 Collaborative Exploration in Stochastic Multi-Player Bandits ACML 2020 Combinatorial Multi-Armed Bandits with Concave Rewards and Fairness Constraints IJCAI 2020 Competing Bandits in Matching Markets AISTATS 2020 Contextual Bandits with Continuous Actions: Smoothing, Zooming, and Adapting JMLR 2020 Contextual Combinatorial Volatile Multi-armed Bandit with Adaptive Discretization AISTATS 2020 Contextual Games: Multi-Agent Learning with Side Information NeurIPS 2020 Cooperative Multi-player Bandit Optimization NeurIPS 2020 Cooperative Online Learning: Keeping your Neighbors Updated ALT 2020 Coordination without communication: optimal regret in two players multi-armed bandits COLT 2020 Covariance-adapting algorithm for semi-bandits with application to sparse outcomes COLT 2020 Crush Optimism with Pessimism: Structured Bandits Beyond Asymptotic Optimality NeurIPS 2020 Customizing ML Predictions for Online Algorithms ICML 2020