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
- November101 - 1:45pm
IFML Seminar
Event DetailsAbstract: This talk will first motivate and illustrate the use of margins as a way to interpret and analyze the...
- November412:15 - 1 pm
IFML Seminar
Event DetailsA central problem in machine learning is as follows: How should we train models using data generated from a collection...
- October2812:15 - 1 pm
IFML Seminar
Event DetailsAbstract: Although Machine learning (ML) algorithms have recently made a huge impact on medical imaging, their development and deployment for...
- October2112:15 - 1 pm
IFML Seminar
Event DetailsRepresentation learning has been widely used in many applications. In this talk, I will present our work which uncovers when...
- October1412:15 - 1 pm
IFML Seminar
Event DetailsWhen Is Partially Observable Reinforcement Learning Not Scary? Abstract: Partially observability is ubiquitous in applications of Reinforcement Learning (RL…
- September2312:15 - 1 pm
ML+ X Seminar
Event DetailsAbstract: In this talk I will present applications and new methods for decomposing higher-order moment tensors into appropriate low-rank representations...
