The PhD defence will be partially digital, in Kristen Nygaards sal (5370), Ole-Johan Dahls hus and streamed directly using Zoom. The host of the session will moderate the technicalities while the chair of the defence will moderate the disputation.
Ex auditorio questions: the chair of the defence will invite the attending audience at Kristen Nygaards sal to ask ex auditorio questions.
Trial lecture
"Equilibrium in Multi-Agent Reinforcement Learning"
Time and place: June 28, 2024 11:15 AM, Kristen Nygaards sal (5370), Ole-Johan Dahls hus/ Zoom
Main research findings
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Machine learning is becoming vital in fields like healthcare, robotics, and online services. This dissertation studies how to design machine learning algorithms that effectively interact with and learn from a diverse group of agents (e.g., humans), which aim to assist, corrupt, or strategically game our algorithms. A central theme of this thesis is the alignment of an algorithm’s objective with that of its human user by actively seeking feedback for its actions. Moreover, we study how to design algorithms that learn and make robust decisions even when malicious agents attempt to impede the algorithm’s learning process. Finally, we study how machine learning algorithms can incentivize agents in online marketplaces to provide truthful information about their products.
Adjudication committee:
- Assistant Professor Debmalya Mandal, University of Warwick, UK
- Assistant Professor Giorgia Ramponi, University of Zurich, Switzerland
- Professor Kyrre Harald Glette, Institute of Informatics, University of Oslo, Norway
Supervisors:
- Professor Christos Dimitrakakis, Department of Informatics, UiO
- Postdoc Anne- Marie George, Department of Informatics, UiO
- Associate Professor Ingrid Chieh Yu, Department of Informatics, UiO
Chair of defence:
Associate Professor Ellen Munthe-Kaas
Contact information at Department: Mozhdeh Sheibani