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Online Algorithms

806 papers

Papers per year

6
24
87
21
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Papers

Improved Regret for Bandit Convex Optimization with Delayed Feedback NIPS 2024
Online Learning of Delayed Choices NIPS 2024
Warm-up Free Policy Optimization: Improved Regret in Linear Markov Decision Processes NIPS 2024
Fair Secretaries with Unfair Predictions NIPS 2024
Optimal Multiclass U-Calibration Error and Beyond NIPS 2024
A Simple and Adaptive Learning Rate for FTRL in Online Learning with Minimax Regret of $\Theta(T^{2/3})$ and its Application to Best-of-Both-Worlds NIPS 2024
Fully Unconstrained Online Learning NIPS 2024
Regret Minimization in Stackelberg Games with Side Information NIPS 2024
Honor Among Bandits: No-Regret Learning for Online Fair Division NIPS 2024
Robust Neural Contextual Bandit against Adversarial Corruptions NIPS 2024
Optimizing the coalition gain in Online Auctions with Greedy Structured Bandits NIPS 2024
Optimal Algorithms for Learning Partitions with Faulty Oracles NIPS 2024
No-Regret Bandit Exploration based on Soft Tree Ensemble Model NIPS 2024
Fast Last-Iterate Convergence of Learning in Games Requires Forgetful Algorithms NIPS 2024
Ensemble sampling for linear bandits: small ensembles suffice NIPS 2024
Contextual Linear Optimization with Bandit Feedback NIPS 2024
Towards Efficient and Optimal Covariance-Adaptive Algorithms for Combinatorial Semi-Bandits NIPS 2024
Stabilizing Linear Passive-Aggressive Online Learning with Weighted Reservoir Sampling NIPS 2024
Warm-starting Push-Relabel NIPS 2024
Contextual Multinomial Logit Bandits with General Value Functions NIPS 2024
Gradient-Variation Online Learning under Generalized Smoothness NIPS 2024
Group-wise oracle-efficient algorithms for online multi-group learning NIPS 2024
Optimal Scalarizations for Sublinear Hypervolume Regret NIPS 2024
Optimal Algorithms for Online Convex Optimization with Adversarial Constraints NIPS 2024
Sparsity-Agnostic Linear Bandits with Adaptive Adversaries NIPS 2024