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7

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Conference papers5 documents

  • Rémy Degenne, Pierre Ménard, Xuedong Shang, Michal Valko. Gamification of pure exploration for linear bandits. International Conference on Machine Learning, 2020, Vienna / Virtual, Austria. ⟨hal-02884330⟩
  • Xuedong Shang, Rianne de Heide, Emilie Kaufmann, Pierre Ménard, Michal Valko. Fixed-confidence guarantees for Bayesian best-arm identification. International Conference on Artificial Intelligence and Statistics, 2020, Palermo, Italy. ⟨hal-02330187v2⟩
  • Xuedong Shang, Emilie Kaufmann, Michal Valko. General parallel optimization without a metric. Algorithmic Learning Theory, 2019, Chicago, United States. ⟨hal-02047225v2⟩
  • Xuedong Shang, Emilie Kaufmann, Michal Valko. A simple dynamic bandit algorithm for hyper-parameter tuning. 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. ⟨hal-02145200⟩
  • Xuedong Shang, Emilie Kaufmann, Michal Valko. Adaptive black-box optimization got easier: HCT only needs local smoothness. European Workshop on Reinforcement Learning, Oct 2018, Lille, France. ⟨hal-01874637⟩

Preprints, Working Papers, ...1 document

  • Xuedong Shang, Han Shao, Jian Qian. Stochastic bandits with vector losses: Minimizing $\ell^\infty$-norm of relative losses. 2020. ⟨hal-02968536⟩

Theses1 document