| General parallel optimization without a metric X Shang, E Kaufmann, M Valko 30th International Conference on Algorithmic Learning Theory, 2019 | 11 | 2019 |
| Adaptive black-box optimization got easier: HCT only needs local smoothness X Shang, E Kaufmann, M Valko 14th European Workshop on Reinforcement Learning, 2018 | 7 | 2018 |
| A simple dynamic bandit algorithm for hyper-parameter tuning X Shang, E Kaufmann, M Valko 6th ICML Workshop on Automated Machine Learning, 2019 | 4 | 2019 |
| Fixed-confidence guarantees for Bayesian best-arm identification X Shang, R de Heide, E Kaufmann, P Ménard, M Valko 23rd International Conference on Artificial Intelligence and Statistics, 2020 | 1 | 2020 |
| Time series clustering D Barbe, A Debant, X Shang https://xuedong.github.io/static/documents/time_series.pdf, 2016 | 1 | 2016 |
| Gamification of pure exploration for linear bandits R Degenne, P Ménard, X Shang, M Valko 37th International Conference on Machine Learning, 2020 | | 2020 |
| Simple (dynamic) bandit algorithms for hyper-parameter optimization X Shang, E Kaufmann, M Valko https://xuedong.github.io/static/documents/dttts.pdf, 2019 | | 2019 |
| Hierarchical bandits for black-box optimization and Monte-Carlo tree search X Shang Team SequeL, Inria Lille - Nord Europe, 2017 | | 2017 |
| Optimal transport geometry for sentiment analysis X Shang Yamamoto-Cuturi Lab., Graduate School of Informatics, Kyoto University, 2016 | | 2016 |
| Recommandation personnalisée X Shang Team Magnet, Inria Lille - Nord Europe, 2015 | | 2015 |
Michal ValkoDeepMind, Paris & Inria France & ENS, MVAVerifierad e-postadress på deepmind.com
