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
- October223 - 4 pm
ML+ X Seminar
Event DetailsNatural language contains information that must be integrated over multiple timescales. To understand how the human brain represents this information...
- October1512 - 1 pm
Foundational Research Seminar
Event DetailsWe consider the problem of quantifying uncertainty for the estimation error of the leading eigenvector from Oja's algorithm for streaming...
- October112 - 3 pm
Ethics/Fairness in AI Seminar
Event DetailsTraditional group fairness definitions are typically defined with respect to a specified classification of people into protected groups, despite many...
- October83 - 4 pm
ML+ X Seminar
Event DetailsAbstract: Understanding how two datasets differ can help us determine whether one dataset under-represents certain sub-populations, and provides insights…
- October112 - 1 pm
Foundational Research Seminar
Event DetailsAbstract: Recent theoretical works on over-parameterized neural nets have focused on two aspects: optimization and generalization. Many existing works that…
- September33 - 4 pm
ML+ X Seminar
Event DetailsAbstract Reinforcement learning (RL) has gained popularity in the research community as a model-free and adaptive control paradigm for the...
