Christos Dimitrakakis
Publications CV TORCS Beliefbox [Availability] [Neuchatel/Oslo/Chalmers] [Email: name at gmail.com - GPG Key]
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.
Current group members
Victor Villin (Neuchatel), PhD student in reinforcement learning.
Andreas Athanasopoulos (Neuchatel), PhD student in fairness.
Milad Maleki Pirbazari (Chalmers), Postdoctoral researcher on reinforcement learning
Anne-Marie George (Oslo), Postdoctoral researcher on computational social choice under uncertainty.
Thomas Kleine Βüning (Oslo), PhD student on collaboration between humans and AI.
Meirav Segal (Oslo), PhD Student on fairness in AI.
Emilio Jorge (Chalmers), PhD student on uncertainty in reinforcement learning .
Past members and collaborators
Hannes Eriksson (Zenseact), (Chalmers PhD student 2017-2023) on risk-sensitive reinforcement learning and autonomous vehicles.
Divya Grover (Boeing), (Chalmers PhD student 2017-2022) on Efficient Bayesian Planning.
Debabrota Basu (INRIA), CR1. (Chalmers Postdoc 2018-2020) on reinforcement learning, differential privacy, adversarial ML.
Aristide Tossou (LG AI). *(Chalmers PhD 2015-2019) on Secure and Private Machine Learning and Decision Making.
David Parkes (Harvard), Professor.
Paul Tylkin (Harvard), PhD student (David Parkes).
Goran Radanovic (MPI SWS), Group Leader.
Yang Liu (UCSC), Professor.
Ronald Ortner (University of Leoben), Professor
Maryam Kamgarpour (EPFL), Assistant Professor
Teaching
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 recent papers
Minimax-Bayes Reinforcement Learning, AISTATS 2023.
On Meritocracy in Optimal Set Selection. Student paper award at EAAMO'22.
SENTINEL: Taming Uncertainty with Ensemble-based Distributional Reinforcement Learning. Presented at UAI2022.
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
Epistemic Risk-Sensitive Reinforcement Learning, ESANN 2020
A Novel Individually Rational Objective In Multi-Agent Multi-Armed Bandit: Algorithms and Regret Bounds, AAMAS 2020
Bayesian Reinforcement Learning via Deep, Sparse Sampling, AISTATS 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.
Achieving privacy in the adversarial multi-armed bandit, AAAI 2017.
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.
Current group members
Victor Villin (Neuchatel), PhD student in reinforcement learning.
Andreas Athanasopoulos (Neuchatel), PhD student in fairness.
Milad Maleki Pirbazari (Chalmers), Postdoctoral researcher on reinforcement learning
Anne-Marie George (Oslo), Postdoctoral researcher on computational social choice under uncertainty.
Thomas Kleine Βüning (Oslo), PhD student on collaboration between humans and AI.
Meirav Segal (Oslo), PhD Student on fairness in AI.
Hannes Eriksson (Chalmers), PhD student on risk-sensitive reinforcement learning and autonomous vehicles.
Emilio Jorge (Chalmers), PhD student on uncertainty in reinforcement learning .
Past members and collaborators
Divya Grover (Boeing), (Chalmers PhD student 2017-2022) on Efficient Bayesian Planning.
Debabrota Basu (INRIA), CR1. (Chalmers Postdoc 2018-2020) on reinforcement learning, differential privacy, adversarial ML.
Aristide Tossou (LG AI). *(Chalmers PhD 2015-2019) on Secure and Private Machine Learning and Decision Making.
David Parkes (Harvard), Professor.
Paul Tylkin (Harvard), PhD student (David Parkes).
Goran Radanovic (MPI SWS), Group Leader.
Yang Liu (UCSC), Professor.
Ronald Ortner (University of Leoben), Professor
Maryam Kamgarpour (EPFL), Assistant Professor
Teaching
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 recent papers
Minimax-Bayes Reinforcement Learning, AISTATS 2023.
On Meritocracy in Optimal Set Selection. Student paper award at EAAMO'22.
SENTINEL: Taming Uncertainty with Ensemble-based Distributional Reinforcement Learning. Presented at UAI2022.
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
Epistemic Risk-Sensitive Reinforcement Learning, ESANN 2020
A Novel Individually Rational Objective In Multi-Agent Multi-Armed Bandit: Algorithms and Regret Bounds, AAMAS 2020
Bayesian Reinforcement Learning via Deep, Sparse Sampling, AISTATS 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.
Achieving privacy in the adversarial multi-armed bandit, AAAI 2017.