Logarithmic Regret for Unconstrained Submodular Maximization Stochastic Bandit
Zhou, J., Gaillard, P., Rahier, T., & Arbel, J. (2024). Logarithmic Regret for Unconstrained Submodular Maximization Stochastic Bandit. arXiv preprint arXiv:2410.08578.
CV
Education
- Ph.D in Version Control Theory, GitHub University, 2018 (expected)
- M.S. in Jekyll, GitHub University, 2014
- B.S. in GitHub, GitHub University, 2012
Work experience
- Spring 2024: Academic Pages Collaborator
- Github University
- Duties includes: Updates and improvements to template
- Supervisor: The Users
- Fall 2015: Research Assistant
- Github University
- Duties included: Merging pull requests
- Supervisor: Professor Hub
- Summer 2015: Research Assistant
- Github University
- Duties included: Tagging issues
- Supervisor: Professor Git
Skills
- Skill 1
- Skill 2
- Sub-skill 2.1
- Sub-skill 2.2
- Sub-skill 2.3
- Skill 3
Publications
Towards Efficient and Optimal Covariance-Adaptive Algorithms for Combinatorial Semi-Bandits
Zhou, J., Gaillard, P., Rahier, T., Zenati, H., & Arbel, J. (2024). Towards efficient and optimal covariance-adaptive algorithms for combinatorial semi-bandits. In The Thirty-eighth Annual Conference on Neural Information Processing Systems.
Talks
Conference Proceeding talk 3 on Relevant Topic in Your Field
Conference proceedings talk at Testing Institute of America 2014 Annual Conference, Los Angeles, CA
Talk 2 on Relevant Topic in Your Field
Talk at London School of Testing, London, UK
Tutorial 1 on Relevant Topic in Your Field
Tutorial at UC-Berkeley Institute for Testing Science, Berkeley CA, USA
Talk 1 on Relevant Topic in Your Field
Talk at UC San Francisco, Department of Testing, San Francisco, California
Teaching
Service and leadership
- Currently signed in to 43 different slack teams