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  • CS Colloquium: Benjamin Eysenbach (CMU) - Self-Supervised Reinforcement Learning

    Thu, Mar 23, 2023 @ 11:00 AM - 12:00 PM

    Thomas Lord Department of Computer Science

    Conferences, Lectures, & Seminars

    Speaker: Benjamin Eysenbach , CMU

    Talk Title: Self-Supervised Reinforcement Learning

    Series: CS Colloquium

    Abstract: Reinforcement learning (RL) promises to harness the power of machine learning to solve sequential decision making problems, with the potential to enable applications ranging from robotics to chemistry. However, what makes the RL paradigm broadly applicable is also what makes it challenging: only limited feedback is provided for learning to select good actions. In this talk, I will discuss how we have made headway of this challenge by designing self-supervised RL methods, ones that can learn representations and skills for acting using unsupervised (reward-free) experience. These skill learning methods are practically-appealing and have since sparked a vibrant area of research. I will also share how we have answered some open theoretical questions in this area.

    This lecture satisfies requirements for CSCI 591: Research Colloquium

    Biography: Benjamin Eysenbach is a final-year PhD student at Carnegie Mellon University. His research has developed machine learning algorithms for sequential decision making. His algorithms not only achieve a high degree of performance, but also carry theoretical guarantees, are typically simpler than prior methods, and draw connections between many areas of ML and CS. Ben is the recipient of the NSF and Hertz graduate fellowships. Prior to the PhD, he was a resident at Google Research and studied math as an undergraduate at MIT.

    Host: Jyo Deshmukh

    Location: Olin Hall of Engineering (OHE) - 132

    Audiences: Everyone Is Invited

    Contact: Assistant to CS chair


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