Claire is a Research Scientist at DeepMind in London UK. She received her PhD from Telecom ParisTech in October 2017, under the guidance of Prof. Olivier Cappé. From January 2018-October 2018, she worked part-time as an Applied Scientist at Amazon in Berlin, while doing a post-doc with Alexandra Carpentier at the University of Magdeburg in Germany.
Her research is on sequential decision making with applications to online recommendation. It mostly spans bandit problems, but Claire's interest also extends to Reinforcement Learning and Learning Theory. While keeping in mind concrete problems -- often inspired by interactions with product teams -- she focuses on theoretical approaches, aiming for provably optimal algorithms.
New project on Off-Policy Evaluation for Contextual Bandits: finite-time confidence intervals can be used to help choosing the best policy out of a finite set of candidates.
Other conference papers
Claire Vernade, Alexandra Carpentier, Giovanni Zappella, Beyza Ermis, and Michael Brueckner. "Contextual bandits under delayed feedback." arXiv preprint arXiv:1807.02089 . ICML 2020
Claire Vernade*, Andras Gyorgy*, Timothy Mann. Andras. Non-Stationary Delayed bandits with Intermediate Observations. arXiv 2006.02119 ICML 2020
Anne-Gaelle Manegueu, Claire Vernade, Alexandra Carpentier, Michal Valko. Stochastic Bandits with Arm-Dependent Delays. arXiv 2006.10459. ICML 2020
Cindy Trinh, Emilie Kaufmann, Claire Vernade, and Richard Combes. "Solving Bernoulli Rank-One Bandits with Unimodal Thompson Sampling." arXiv preprint arXiv:1912.03074 ALT 2020
Russac, Yoan, Claire Vernade, and Olivier Cappé. "Weighted Linear Bandits for Non-Stationary Environments." In Advances in Neural Information Processing Systems, pp. 12017-12026. 2019.
Achab, Mastane; Clémençon, Stephan; Garivier, Aurélien; Sabourin, Anne; Vernade, Claire; "Max K-armed bandit: On the ExtremeHunter algorithm and beyond,Joint European Conference on Machine Learning and Knowledge Discovery in Databases (2017)
Vernade, Claire; Cappé, Olivier; Perchet, Vianney; "Stochastic bandit models for delayed conversions,arXiv preprint arXiv:1706.09186 (2017)
Kwon, Joon; Perchet, Vianney; Vernade, Claire; "Sparse stochastic bandits". COLT 2017. arXiv preprint arXiv:1706.01383
Katariya, Sumeet; Kveton, Branislav; Szepesvári, Csaba; Vernade, Claire; Wen, Zheng; "Bernoulli Rank-$1 $ Bandits for Click Feedback". IJCAI 2017. arXiv preprint arXiv:1703.06513 (2017)
Katariya, Sumeet; Kveton, Branislav; Szepesvari, Csaba; Vernade, Claire; Wen, Zheng; "Stochastic rank-1 bandits". AISTATS 2017. arXiv preprint arXiv:1608.03023
Lagrée, Paul; Vernade, Claire; Cappé, Olivier; "Multiple-play bandits in the position-based model". NIPS 2016
Vernade, Claire; Cappé, Olivier; "Learning from missing data using selection bias in movie recommendation". IEEE International Conference on Data Science and Advanced Analytics (2015)
Kveton, Branislav, Zheng Wen, Yasin Abbasi Yadkori, Mohammad Ghavamzadeh, and Claire Vernade. "Multivariate digital campaign content testing utilizing rank-1 best-arm identification." U.S. Patent Application 15/944,980, filed October 10, 2019.
March 2020: Workshop on Optimisation and Machine Learning at CIRM (France)
November 26th 2019: Seminaire du departement d'informatique de l'ENS Rennes (in French)
March 2018: Invited talk at UW (invited by Joseph Salmon and Zaid Harchaoui)
October 20th 2017: PhD Defense
June 2016: Workshop on Machine Learning for Online Advertising at ICML 2016