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Online Algorithms

2190 directly classified papers

Papers per year

Papers

Online Learning of Partitions in Additively Separable Hedonic Games IJCAI 2024
Contract Scheduling with Distributional and Multiple Advice IJCAI 2024
On Differentially Private Subspace Estimation in a Distribution-Free Setting NIPS 2024
Kernel-Based Function Approximation for Average Reward Reinforcement Learning: An Optimist No-Regret Algorithm NIPS 2024
Piecewise-Stationary Bandits with Knapsacks NIPS 2024
Sifter: An Inversion-Free and Large-Capacity Programmable Packet Scheduler NSDI 2024
Sequential Probability Assignment with Contexts: Minimax Regret, Contextual Shtarkov Sums, and Contextual Normalized Maximum Likelihood NIPS 2024
Online Submodular Maximization via Adaptive Thresholds IJCAI 2024
Near-Optimal Dynamic Regret for Adversarial Linear Mixture MDPs NIPS 2024
Tight Rates for Bandit Control Beyond Quadratics NIPS 2024
Greedy-Based Online Fair Allocation with Adversarial Input: Enabling Best-of-Many-Worlds Guarantees AAAI 2024
Stakeholder-oriented Decision Support for Auction-based Federated Learning IJCAI 2024
Achieving $\tilde{O}(1/\epsilon)$ Sample Complexity for Constrained Markov Decision Process NIPS 2024
Learning with Posterior Sampling for Revenue Management under Time-varying Demand IJCAI 2024
Optimistic Search: Change Point Estimation for Large-scale Data via Adaptive Logarithmic Queries JMLR 2024
Online Sampling and Decision Making with Low Entropy IJCAI 2024
Structured Dynamic Pricing: Optimal Regret in a Global Shrinkage Model JMLR 2024
Queueing Matching Bandits with Preference Feedback NIPS 2024
Optimal Learning Policies for Differential Privacy in Multi-armed Bandits JMLR 2024
Sparse Recovery With Multiple Data Streams: An Adaptive Sequential Testing Approach JMLR 2024
Contextual Bandits with Packing and Covering Constraints: A Modular Lagrangian Approach via Regression JMLR 2024
Metric Distortion with Elicited Pairwise Comparisons IJCAI 2024
Multiobjective Lipschitz Bandits under Lexicographic Ordering AAAI 2024
Overcoming Brittleness in Pareto-Optimal Learning Augmented Algorithms NIPS 2024
B-ary Tree Push-Pull Method is Provably Efficient for Distributed Learning on Heterogeneous Data NIPS 2024