IFML Seminar
IFML Seminar: 10/03/25 - Successor Measures and Self-supervised Reinforcement Learning
Abstract: We introduce a method for learning behavioral foundation models using the successor measure. We show that any visitation distribution can be represented using an affine combination of policy-independent basis functions. By learning these...
Upcoming Events
- October312:15 - 1:15pm
IFML Seminar
Event DetailsAbstract: We introduce a method for learning behavioral foundation models using the successor measure. We show that any visitation distribution...
- October1012:15 - 1:15pm
IFML Seminar
Event DetailsABSTRACT: What would it mean for AI to understand skilled human activity? In augmented reality (AR), a person wearing smart...
- October2412:15 - 1:15pm
IFML Seminar
Event DetailsAbstract: Learning from sequential, temporally-correlated data is a core facet of modern machine learning and statistical modeling. Yet our fundamental...
Past Events
- March4throughMarch6
Public Lecture
Event DetailsJoin us for the 2025 AI + Robotics Research Symposium -- three days of talks, panels, presentations, and networking opportunities...
- February2812:15 - 1:15pm
IFML Seminar
Event DetailsAbstract: Learning representations that generalize across diverse downstream tasks is a fundamental challenge in machine learning. Contrastive learning, self…
- February20throughFebruary21
Workshop
Event Details2 days of academic research presentations on the mathematical theory underpinning modern machine learning paradigms. Presented by The University of...
- February1412:15 - 1:15pm
IFML Seminar
Event DetailsAbstract: Mainstream artificial neural network models, such as Deep Neural Networks (DNNs) are computation-heavy and energy-hungry. Weightless Neural…
- February712:15 - 1:15pm
IFML Seminar
Event DetailsAbstract: Reinforcement Learning from Human Feedback (RLHF) has become the predominant method for aligning large language models (LLMs) to be...
- January3112:15 - 1:15pm
IFML Seminar
Event DetailsAbstract: We pose a fundamental question in computational learning theory: can we efficiently test whether a training set satisfies the...
