IFML Seminar
IFML Seminar: 10/17/25 - Sample-Efficient Personalized Reward Models for Pluralistic Alignment
Abstract: Large pre-trained models trained on internet-scale data are often not ready for deployment out-of-the-box. They are heavily fine-tuned and aligned using large quantities of human preference data, usually elicited using pairwise comparisons. While...
Upcoming Events
- October1712:15 - 1:15pm
IFML Seminar
Event DetailsAbstract: Large pre-trained models trained on internet-scale data are often not ready for deployment out-of-the-box. They are heavily fine-tuned and...
- 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...
- November712:15 - 1:15pm
IFML Seminar
Event DetailsAbstract: Retraining a model using its own predictions together with the original, potentially noisy labels is a well-known strategy for...
Past Events
- 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...
- 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...
- September1912:15 - 1:15pm
IFML Seminar
Event DetailsAbstract: LLMs have not only revolutionized text-based natural language processing, but their multimodal extensions have proven to be extremely powerful...
- September1212:15 - 1:15pm
IFML Seminar
Event DetailsAbstract: A fundamental problem in optimization is finding an ε-first-order stationary point of a smooth function using only gradient information...
- July29All dayEvent Details
AUSTIN, Texas — A National Science Foundation artificial intelligence institute based at The University of Texas at Austin will receive...
- May212:15 - 1:15pm
IFML Seminar
Event DetailsAbstract: We revisit the fundamental problem of learning with distribution shift, where a learner is given labeled samples from training...
