Online Learning

Ahpatron: A New Budgeted Online Kernel Learning Machine with Tighter Mistake Bound AAAI 2024 An Online Bootstrap for Time Series AISTATS 2024 Apple Tasting: Combinatorial Dimensions and Minimax Rates COLT 2024 A Simple and Adaptive Learning Rate for FTRL in Online Learning with Minimax Regret of Θ(T^2/3) and its Application to Best-of-Both-Worlds NeurIPS 2024 A Theory of Optimistically Universal Online Learnability for General Concept Classes NeurIPS 2024 Autobidders with Budget and ROI Constraints: Efficiency, Regret, and Pacing Dynamics COLT 2024 Autoregressive Bandits AISTATS 2024 BanditQ:Fair Bandits with Guaranteed Rewards UAI 2024 Bandits with Knapsacks and Predictions UAI 2024 Bandits with Ranking Feedback NeurIPS 2024 Barely Random Algorithms and Collective Metrical Task Systems NeurIPS 2024 Bayesian Online Learning for Consensus Prediction AISTATS 2024 Binary Search with Distributional Predictions NeurIPS 2024 BPO: Staying Close to the Behavior LLM Creates Better Online LLM Alignment EMNLP 2024 Can Probabilistic Feedback Drive User Impacts in Online Platforms? AISTATS 2024 Caravan: Practical Online Learning of In-Network ML Models with Labeling Agents OSDI 2024 Combinatorial Bandits for Maximum Value Reward Function under Value-Index Feedback ICLR 2024 Combinatorial Stochastic-Greedy Bandit AAAI 2024 Communication Bounds for the Distributed Experts Problem NeurIPS 2024 Communication-Efficient Federated Non-Linear Bandit Optimization ICLR 2024 Conformal Inference for Online Prediction with Arbitrary Distribution Shifts JMLR 2024 Contextual Active Model Selection NeurIPS 2024 Contextual Bandits with Packing and Covering Constraints: A Modular Lagrangian Approach via Regression JMLR 2024 Contextual Decision-Making with Knapsacks Beyond the Worst Case NeurIPS 2024 Contextual Pandora’s Box AAAI 2024