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Budgeted Online Influence Maximization.

, , , and . ICML, volume 119 of Proceedings of Machine Learning Research, page 7620-7631. PMLR, (2020)

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Finding the bandit in a graph: Sequential search-and-stop., , and . AISTATS, volume 89 of Proceedings of Machine Learning Research, page 1668-1677. PMLR, (2019)Exploiting structure of uncertainty for efficient matroid semi-bandits., , and . ICML, volume 97 of Proceedings of Machine Learning Research, page 5123-5132. PMLR, (2019)Covariance-adapting algorithm for semi-bandits with application to sparse outcomes., , and . COLT, volume 125 of Proceedings of Machine Learning Research, page 3152-3184. PMLR, (2020)Exploiting Structure of Uncertainty for Efficient Combinatorial Semi-Bandits., , and . CoRR, (2019)When Combinatorial Thompson Sampling meets Approximation Regret.. NeurIPS, (2022)Statistical Efficiency of Thompson Sampling for Combinatorial Semi-Bandits., , , and . NeurIPS, (2020)Fast Rates for Maximum Entropy Exploration., , , , , , , , , and . ICML, volume 202 of Proceedings of Machine Learning Research, page 34161-34221. PMLR, (2023)Efficient Learning in Stochastic Combinatorial Semi-Bandits. (Apprentissage Efficient dans les Problèmes de Semi-Bandits Stochastiques Combinatoires).. Paris-Saclay University, France, (2020)Online A-Optimal Design and Active Linear Regression., , , and . ICML, volume 139 of Proceedings of Machine Learning Research, page 3374-3383. PMLR, (2021)Budgeted Online Influence Maximization., , , and . ICML, volume 119 of Proceedings of Machine Learning Research, page 7620-7631. PMLR, (2020)