← Optimization & Theory
Machine Learning › Optimization & Theory ›

Online Algorithms

803 directly classified papers

Papers per year

Papers

Regret Bounds for Online Kernel Selection in Continuous Kernel Space AAAI 2021
Decentralized Learning in Online Queuing Systems NIPS 2021
Regret Bounds for Batched Bandits AAAI 2021
Heterogeneous Multi-player Multi-armed Bandits: Closing the Gap and Generalization NIPS 2021
Stochastic Graphical Bandits with Adversarial Corruptions AAAI 2021
Near-Optimal Regret Bounds for Contextual Combinatorial Semi-Bandits with Linear Payoff Functions AAAI 2021
ACAT-G: An Interactive Learning Framework for Assisted Response Generation AAAI 2021
Confidence-Budget Matching for Sequential Budgeted Learning ICML 2021
AI for Social Good: Between My Research and the Real World AAAI 2021
Federated Multi-Armed Bandits AAAI 2021
Online A-Optimal Design and Active Linear Regression ICML 2021
A Contextual Bandit Bake-off JMLR 2021
Dynamic Planning and Learning under Recovering Rewards ICML 2021
The Symmetry between Arms and Knapsacks: A Primal-Dual Approach for Bandits with Knapsacks ICML 2021
MOTS: Minimax Optimal Thompson Sampling ICML 2021
Almost Optimal Anytime Algorithm for Batched Multi-Armed Bandits ICML 2021
Exponential Weights Algorithms for Selective Learning COLT 2021
Adaptive Discretization for Adversarial Lipschitz Bandits COLT 2021
Optimal Streaming Algorithms for Multi-Armed Bandits ICML 2021
Towards Reducing Biases in Combining Multiple Experts Online IJCAI 2021
Littlestone Classes are Privately Online Learnable NIPS 2021
Addressing the Long-term Impact of ML Decisions via Policy Regret IJCAI 2021
Tsallis-INF: An Optimal Algorithm for Stochastic and Adversarial Bandits JMLR 2021
Refined approachability algorithms and application to regret minimization with global costs JMLR 2021
Dominate or Delete: Decentralized Competing Bandits in Serial Dictatorship AISTATS 2021