Join us on May 28, when Guest Lecturer Alberto Maria Metelli will hold a Research Talk on Learning with Diverse Human Feedback!

Reinforcement learning is built around the idea of a reward function that defines the task to be solved. However, designing an effective reward function is often challenging and requires substantial expert knowledge. In many cases, humans are better suited to provide indirect feedback on an agent’s behavior rather than assigning explicit numerical rewards. Such feedback may take the form of preferences, corrections, or demonstrations. In this talk, we present core ideas for unifying diverse forms of human feedback through rationality models of behavior. Particular attention is devoted to preference-based feedback, for which we discuss several modeling approaches, the corresponding learning problems, and algorithms for addressing them.

About Alberto Maria Metelli

Alberto Maria Metelli is an Assistant Professor of Information Processing Systems with the Dipartimento di Elettronica, Informazione e Bioingegneria, in the Artificial Intelligence and Robotics Laboratory at the Politecnico di Milano. He obtained his PhD (cum Laude) in Information Technology in March 2021, which was awarded the “Premio NeoDottori di Ricerca Marco Cadoli 2021” as the best Italian PhD thesis in AI. He is the co-founder of ML Cube S.r.l., an innovative start-up for machine learning lifecycle management. He won the FIS Starting Grant in 2025, a prestigious Italian award for fundamental research. His research revolves around reinforcement learning. He is a member of the ELLIS Society and co-author of over 50 papers in top venues (JMLR, MLJ, ICML, NeurIPS, AAAI)

Join us Online

The lecture will be streamed via Zoom (Meeting ID: 615 2496 0493, Password: P2LQU3XH)

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