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Adaptive black-box optimization got easier: HCT only needs local smoothness

Xuedong Shang , Emilie Kaufmann , Michal Valko
European Workshop on Reinforcement Learning, Oct 2018, Lille, France
Conference papers hal-01874637v1
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Gamification of pure exploration for linear bandits

Rémy Degenne , Pierre Ménard , Xuedong Shang , Michal Valko
ICML 2020 - International Conference on Machine Learning, Aug 2020, Vienna / Virtual, Austria
Conference papers hal-02884330v1
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Adaptive methods for optimization in stochastic environments

Xuedong Shang
Artificial Intelligence [cs.AI]. Université de Lille, 2021. English. ⟨NNT : 2021LILUB007⟩
Theses tel-03466525v4
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Fixed-confidence guarantees for Bayesian best-arm identification

Xuedong Shang , Rianne de Heide , Emilie Kaufmann , Pierre Ménard , Michal Valko
International Conference on Artificial Intelligence and Statistics, 2020, Palermo, Italy
Conference papers hal-02330187v2
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A simple dynamic bandit algorithm for hyper-parameter tuning

Xuedong Shang , Emilie Kaufmann , Michal Valko
Workshop on Automated Machine Learning at International Conference on Machine Learning, AutoML@ICML 2019 - 6th ICML Workshop on Automated Machine Learning, Jun 2019, Long Beach, United States
Conference papers hal-02145200v1
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General parallel optimization without a metric

Xuedong Shang , Emilie Kaufmann , Michal Valko
Algorithmic Learning Theory, 2019, Chicago, United States
Conference papers hal-02047225v2
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Stochastic bandits with vector losses: Minimizing $\ell^\infty$-norm of relative losses

Xuedong Shang , Han Shao , Jian Qian
Preprints, Working Papers, ... hal-02968536v1