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
- April712:15 - 1 pm
IFML Seminar
Event DetailsAbstract: Machine learning systems are built using large troves of training data that may contain private or copyrighted content. In...
- March3112:15 - 1 pm
IFML Seminar
Event DetailsAbstract: Unsupervised learning with functional data is an emerging paradigm of machine learning research with applications to computer vision, climate...
- March313:15 - 4 pm
IFML Seminar
Event DetailsAbstract: Deep learning is delivering unprecedented performance when applied to various data modalities, yet there are data distributions over which...
- March302 - 3 pmEvent Details
Abstract: Magnetic Resonance Imaging (MRI) enables non-invasive measurement of anatomy with a wide range of image contrast, but incurs significant...
- March2412:15 - 1 pm
IFML Seminar
Event DetailsAbstract: A brain-machine interface (BMII) is a system that enables users to interact with computers and robots through the voluntary...
- March312:15 - 1 pm
IFML Seminar
Event DetailsAbstract: Customer statistics collected in several real-world systems have reflected that users often prefer eliciting their liking for a given...
