Embedded Files
Publications CV TORCS Beliefbox [Availability] [Neuchatel/Oslo/Chalmers] [Email: full.name at gmail.com - GPG Key]
Current group members
Current group members
Hortence Nana (Neuchatel), PhD student
Elif Ylimaz (Neuchatel), PhD student
Jakub Tluczek (Neuchatel), PhD student
Victor Villin (Neuchatel), PhD student on reinforcement learning.
Andreas Athanasopoulos (Neuchatel), PhD student on fairness.
Emilio Jorge (Chalmers), PhD student on uncertainty in reinforcement learning .
Past members and collaborators
Past members and collaborators
Anne-Marie George (Oslo), Ass. Professor, ex-PostDoc.
Meirav Segal (Uni-Zurich), PostDoc, ex-PhD.
Thomas Kleine Βüning (ETHZ), PostDoc, ex-PhD.
Milad Maleki Pirbazari (Volvo), ex-PostDoc
Hannes Eriksson (Zenseact), ex-PhD
Divya Grover (Boeing), ex-PhD
Debabrota Basu (INRIA), CR1., ex-PostDoc
Aristide Tossou (LG AI). ex-PhD
David Parkes (Harvard), Professor.
Paul Tylkin (MIT), ex-PhD (David Parkes).
Goran Radanovic (MPI SWS), Group Leader.
Yang Liu (UCSC), Professor.
Ronald Ortner (University of Leoben), Professor
Maryam Kamgarpour (EPFL), Assistant Professor
Positions
Positions
PhD position in behavioural modelling for avian pest control.
Research topics
Research topics
Reinforcement learning
Collaborative AI
Efficient Exploration
Safety and Risk
Differential privacy
Fairness
Bayesian inference and uncertainty
I offer MSc theses in the above areas.
Teaching
Teaching
Machine Learning: Theory, Fairness, Privacy (MSc, Neuchatel, Autumn 24)
Introduction to Machine Learning (BSc, Neuchatel, Autumn 24)
Artificial Intelligence (BSc, Neuchatel, Spring 24)
Machine Learning and Data Mining (MSc, Neuchatel, Autumn 23)
Reinforcement Learning and Decision Making Under Uncertainty (Neuchatel, Spring 23)
Advanced topics in Learning and Decision Making (Neuchatel, Spring 23)
Fairness and Privacy in Machine Learning (Neuchatel, Autumn 22)
Advanced Topics in Learning, Privacy and Fairness (Neuchatel, Autumn 22)
Intuitive statistics (digital skills, data visualisation) (Neuchatel, Autumn 22)
Selected papers
Selected papers
Probably Correct Optimal Stable Matching for Two-Sided Market Under Uncertainty, AAMAS 2025.
A minimax approach to ad-hoc teamwork, AAMAS 2025.
Environment Design for Inverse Reinforcement Learning, ICML 2024
Eliciting Kemeny Rankings, AAMAS 2024
Minimax-Bayes Reinforcement Learning, AISTATS 2023.
On Meritocracy in Optimal Set Selection. Student paper award at EAAMO'22.
Interactive Inverse Reinforcement Learning for Cooperative Games, Cooperative-AI@NeurIPS 2021 (Best Paper Award). Presented at ICML2022.
Inferential Induction, I Cannot Believe It's Not Better@NeurIPS 2020
Bayesian fairness, AAAI 2019.
Multi-View Decision Processes, [poster] [video] [slides] NIPS 2017.
Calibrated fairness In bandits, FATML-17.
Differential privacy for Bayesian Inference through posterior sampling, JMLR, 2017.
Google Sites
Report abuse