Claire Vernade


Claire is a Group Leader at the University of Tuebingen, in the Cluster of Excellence Machine Learning for Science. She was awarded an Emmy Noether award under the AI Initiative call in 2022.

Her research is on sequential decision making. It mostly spans bandit problems, and theoretical Reinforcement Learning, but her research interests extend to Learning Theory and principled learning algorithms. While keeping in mind concrete problems, she focuses on theoretical approaches, aiming for provably optimal algorithms.

Previously, she was Research Scientist at DeepMind in London UK since November 2018 in the Foundations team lead by Prof. Csaba Szepesvari. She did a post-doc in 2018 with Prof. Alexandra Carpentier at the University of Magdeburg in Germany while working part-time as an Applied Scientist at Amazon in Berlin. She received her PhD from Telecom ParisTech in October 2017, under the guidance of Prof. Olivier Cappé.

contact: first.last @ gmail . com



  • I left Deepmind in December 2022 and joined the University of Tuebingen, Germany as a Group Leader in January 2022. My group is supported by an Emmy Noether award for the project "Foundations for Lifelong Reinforcement Learning" within the AI Initiative.

I am hiring PhD students within the IMPRS-IS program, the deadline is on November 1st 2022. Please reach out if you have questions.

  • I will be attending NeurIPS 2022 in New Orleans, please reach out if you would like to meet :)


Conference papers

  • Ilja Kuzborskij, Claire Vernade, András György, Csaba Szepesvári. "Confident Off-Policy Evaluation and Selection through Self-Normalized Importance Weighting". AISTATS 2021 (arXiv link)

  • Johannes Kirschner, Tor Lattimore, Claire Vernade, Csaba Szepesvári. Asymptotically Optimal Information-Directed Sampling. COLT 2021. arXiv link

  • Claire Vernade, Alexandra Carpentier, Giovanni Zappella, Beyza Ermis, and Michael Brueckner. "Contextual bandits under delayed feedback." arXiv:1807.02089 . ICML 2020

  • Claire Vernade*, Andras Gyorgy*, Timothy Mann. 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" ECML 2017

  • Vernade, Claire; Cappé, Olivier; Perchet, Vianney; "Stochastic bandit models for delayed conversions" UAI 2017

  • Kwon, Joon; Perchet, Vianney; Vernade, Claire; "Sparse stochastic bandits". COLT 2017.

  • Katariya, Sumeet; Kveton, Branislav; Szepesvári, Csaba; Vernade, Claire; Wen, Zheng; "Bernoulli Rank-$1 $ Bandits for Click Feedback". IJCAI 2017

  • Katariya, Sumeet; Kveton, Branislav; Szepesvari, Csaba; Vernade, Claire; Wen, Zheng; "Stochastic rank-1 bandits". AISTATS 2017.

  • 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)

PhD Thesis Bandit models for interactive applications (Published version with additional introduction in French)


  • 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.

Students and mentoring

As a Junior Researcher, I do not yet have the chance of officially supervising students. However, I did get the opportunity to advise some on various projects, including the MVA Masters program course on Graphs in Machine Learning taught by Prof. Michal Valko.

  • Yoan Russac (intern at Amazon Berlin in 2018 -- now, RE at Twitter, London UK)

  • Clémence Réda (Master project in 2019 -- now, PhD student at INSERM Paris)

  • Abel Adary, Louis Fournier (Master project in 2020)

  • Antoine Moulin (Master project in 2020, now PhD student with Gergely Neu in Barcelona)

  • Yoann Lemesle (Bachelor year-long project 2020-2021)

  • Jeremias Knoblauch (Deepmind Research intern 2020 -- Lecturer at UCL)

  • Flore Sentenac (DeepMind Research intern 2022 -- PhD student at CREST Paris)

Selected Talks

  • 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 University of Washington (invited by Joseph Salmon and Zaid Harchaoui)

  • October 20th 2017: PhD Defense

  • June 2016: Workshop on Machine Learning for Online Advertising at ICML 2016

Service and Community

I am a reviewer or senior PC for all major conferences: NeurIPS, ICML, AISTATS, COLT, ALT, ICLR. I have been an Area Chair for ICLR 2021 and NeurIPS 2021. I am a Program Chair for the ICLR Blogpost track.

I strongly support community groups, in particular towards better diversity in our extended field (Computer Science and Stats). I have volunteered and area chaired for the WiML workshop many times and I am co-leading with Ruth Urner the WiML-T effort for Women in Learning Theory .