ASSET Seminar: Scallop: A Language for Neuro-Symbolic Programming, Mayur Naik (University of Pennsylvania)
ABSTRACT: Neurosymbolic learning is an emerging paradigm which, at its core, combines the otherwise complementary worlds of classical algorithms and deep learning; in doing so, it ushers in more accurate, […]
ASSET Seminar: Building Safe Autonomous Systems, Rahul Mangharam (University of Pennsylvania)
ABSTRACT: Balancing performance and safety are crucial to deploying autonomous vehicles in multi-agent environments. In particular, autonomous racing is a domain that penalizes safe but conservative policies, highlighting the need […]
ASSET Seminar: Decision-Aware Learning for Global Health Supply Chains, Osbert Bastani (University of Pennsylvania)
ABSTRACT: Machine learning algorithms are increasingly used in conjunction with optimization to guide decision making. A key challenge is aligning the machine learning loss with the decision-making loss. Existing solutions […]
ASSET Seminar: Building certifiably safe and correct large-scale autonomy, Chuchu Fan (Massachusetts Institute of Technology)
ABSTRACT: The introduction of machine learning (ML) and artificial intelligence (AI) creates unprecedented opportunities for achieving full autonomy. However, learning-based methods in building autonomous systems can be extremely brittle in […]
ASSET Seminar: How to Design Molecules that Dock Well but Can’t Exist, Jacob Gardner, Ph.D.
ABSTRACT:BIO Machine learning has become an indispensable aid to researchers developing the next generation of novel therapeutics. In this talk, I will discuss how some of the most important problems in […]
ASSET Seminar: New approaches to detecting and adapting to domain shifts in machine learning, Zico Kolter, Ph.D. (Carnegie Mellon University)
ABSTRACT: Machine learning systems, in virtually every deployed system, encounter data from a qualitatively different distribution than what they were trained upon. Effectively dealing with this problem, known as domain […]
ASSET Seminar: What Transfers in Transfer Learning?, Eric Wong (University of Pennsylvania)
Abstract: Recently, the transfer learning paradigm has seen a surge of interest due to its impressive capabilities in vision and language. Models are pretrained on ever-growing datasets with enormous parameter […]
ASSET Seminar: Learning with Small Data, Pratik Chaudhari (University of Pennsylvania)
Abstract: The relevant limit for machine learning is not N → infinity but instead N → 0. The human visual system is proof that it is possible to learn categories […]
ASSET Seminar: Equivariance in Deep Learning, Kostas Daniilidis (University of Pennsylvania)
ABSTRACT Traditional convolutional networks exhibit unprecedented robustness to intraclass nuisances when trained on big data. Generalization with respect to geometric transformations has been achieved via expensive data augmentation. It has been shown recently that […]
ASSET Seminar: Explainable AI via Semantic Information Pursuit (René Vidal, Johns Hopkins University)
Presentation Abstract: There is a significant interest in developing ML algorithms whose final predictions can be explained in domain-specific terms that are understandable to a human. Providing such an “explanation” […]