Snabbfakta
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- Villeneuve-d'Ascq
Ansök senast: 2024-08-31
PhD Position F/ M Responsible Reinforcement Learning: Robustness and Privacy in and by Sequential Decision Making
Contexte et atouts du poste
In his/her journey to the doctoral thesis, the candidate will be supported by PEPR project FOUNDRY, and supervised by . Debabrota and Emilie are affiliated with the
As RL algorithms are getting deployed in real-life the questions of responsible deployment, such as robustness to noise and perturbation to the feedback from environment, and privacy if users are involved in the environment yielding data.
Our works have shown that for structure-less and linear settings of multi-armed bandits and active testing (aka pure exploration) imposing privacy yields two regimes of performance. For the regime used in practice, privacy can be preserved without loss of utility. But our existing approach is not directly applicable to more practically appealing settings of RL, like MDPs or bandits with side-information (aka contexts). In these settings, there is a gap between achievable performances and the algorithms. Thus, we want to study whether the cost of privacy in contextual bandits and MDPs, and also to design optimal, computationally efficient algorithms.
Similarly, we have studied impact of unbounded corruption in feedback and safety constraints in stochastic multi-armed bandits and active testing (aka pure exploration). We want to understand how do they impact more structured RL problems and how can we design optimal algorithms in these setting.
The project is expected to simulate the existing and new collaborations with researchers and groups working on privacy-preserving machine learning, robustness, adaptive testing, and reinforcement learning. In future, the candidate will be encouraged to not only work with us but collaborate internationally. The candidate will also be part of the
Mission confiée
This position is entirely dedicated to do one's Ph.D. thesis. French rules put a strong emphasis on the fact that the Ph.D. is completed within 3 full years of studies.
It is also possible to teach up to a reasonnable amount of time per year (say 30 hours / year to give a rough idea of what we mean by "reasonable"). More details about the topic of the Ph.D. is available at
Principales activités
All research activities, that is bibliographical search, proposing original ideas related to the topic of the Ph.D. and developing them, presenting the work in the Scool seminar, workshops and conferences. The candidate should aim to publish the research results in premier conferences and journals of our field of research (e.g. ICML, NeurIPS, COLT, IJCAI, AAAI, JMLR). Since the work involves and impacts the responsible AI in general, the successful candidate should collaborate in writing scientific articles aiming towards the larger audience.
Compétences
The candidate should preferably have the following skills:
Please follow the instructions given in to set up your application file.
In brief, the application of the candidate should include his/her CV, an application letter, (two or more) recommendation letters, and the school transcripts. It is reccommended that the candidate contacts Debabrota and Emilie while preparing the application.
The deadline for application is 15th July, 2024.
Avantages
Rémunération
1st and 2nd year : 2100 € (gross monthly salarye)
3rd year : 2190 € (gross monthly salary)
