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Online Algorithms

803 directly classified papers

Papers per year

Papers

SPEED: Experimental Design for Policy Evaluation in Linear Heteroscedastic Bandits AISTATS 2024
Fully Unconstrained Online Learning NIPS 2024
Fast Last-Iterate Convergence of Learning in Games Requires Forgetful Algorithms NIPS 2024
Optimal Algorithms for Online Convex Optimization with Adversarial Constraints NIPS 2024
Sparsity-Agnostic Linear Bandits with Adaptive Adversaries NIPS 2024
Warm-starting Push-Relabel NIPS 2024
Online Learning of Delayed Choices NIPS 2024
New Classes of the Greedy-Applicable Arm Feature Distributions in the Sparse Linear Bandit Problem AAAI 2024
Improved Bandits in Many-to-One Matching Markets with Incentive Compatibility AAAI 2024
Online Conversion Rate Prediction via Multi-Interval Screening and Synthesizing under Delayed Feedback AAAI 2024
Optimal Multiclass U-Calibration Error and Beyond NIPS 2024
How does Inverse RL Scale to Large State Spaces? A Provably Efficient Approach NIPS 2024
No-Regret Algorithms for Safe Bayesian Optimization with Monotonicity Constraints AISTATS 2024
Think Before You Duel: Understanding Complexities of Preference Learning under Constrained Resources AISTATS 2024
Online Submodular Maximization via Online Convex Optimization AAAI 2024
A Survey on Multi-player Bandits JMLR 2024
No Internal Regret with Non-convex Loss Functions AAAI 2024
Learning to Price Homogeneous Data NIPS 2024
Query Routing for Homogeneous Tools: An Instantiation in the RAG Scenario EMNLP 2024
Hierarchize Pareto Dominance in Multi-Objective Stochastic Linear Bandits AAAI 2024
PRODuctive bandits: Importance Weighting No More NIPS 2024
Real-Time Selection Under General Constraints via Predictive Inference NIPS 2024
Fair Secretaries with Unfair Predictions NIPS 2024
Gradient-Variation Online Learning under Generalized Smoothness NIPS 2024
Dynamic Regret of Adversarial MDPs with Unknown Transition and Linear Function Approximation AAAI 2024