Online Algorithms

Improved Analysis for Dynamic Regret of Strongly Convex and Smooth Functions L4DC 2021 Improved Regret Bound and Experience Replay in Regularized Policy Iteration ICML 2021 Improved Regret Bounds for Tracking Experts with Memory NeurIPS 2021 Improved Regret Bounds of Bilinear Bandits using Action Space Analysis ICML 2021 Improving Welfare in One-Sided Matchings using Simple Threshold Queries IJCAI 2021 Incentivized Bandit Learning with Self-Reinforcing User Preferences ICML 2021 Incentivizing Compliance with Algorithmic Instruments ICML 2021 Increasing Iterate Averaging for Solving Saddle-Point Problems AAAI 2021 Information Directed Sampling for Sparse Linear Bandits NeurIPS 2021 Instance-Wise Minimax-Optimal Algorithms for Logistic Bandits AISTATS 2021 Jerk-limited Real-time Trajectory Generation with Arbitrary Target States RSS 2021 Jointly Learning Prices and Product Features IJCAI 2021 Joint Online Learning and Decision-making via Dual Mirror Descent ICML 2021 Last Round Convergence and No-Dynamic Regret in Asymmetric Repeated Games ALT 2021 Lazy OCO: Online Convex Optimization on a Switching Budget COLT 2021 Learning-Augmented Dynamic Power Management with Multiple States via New Ski Rental Bounds NeurIPS 2021 Learning Augmented Methods for Matching: Improving Invasive Species Management and Urban Mobility AAAI 2021 Learning Equilibria in Matching Markets from Bandit Feedback NeurIPS 2021 Learning in Multi-Stage Decentralized Matching Markets NeurIPS 2021 Learning Online Algorithms with Distributional Advice ICML 2021 Learning Partially Observed Linear Dynamical Systems from Logarithmic Number of Samples L4DC 2021 Learning-to-learn non-convex piecewise-Lipschitz functions NeurIPS 2021 Learning to Price Against a Moving Target ICML 2021 Learning with Comparison Feedback: Online Estimation of Sample Statistics ALT 2021 Lenient Regret and Good-Action Identification in Gaussian Process Bandits ICML 2021