ASSET/IBI Symposium on Trustworthy AI for Health Care

Organizers: Rajeev Alur (Penn Engineering), John Holmes (PSOM), Insup Lee (Penn Engineering), Qi Long (PSOM), Marylyn Richie (PSOM) Event Description: Artificial intelligence and machine learning promise to revolutionize nearly every field, […]

ASSET Seminar: “Getting Computers to Do What We Want: Programming Meets Machine Learning” (Michael Littman, Brown University)

ABSTRACT: It is immensely empowering to delegate information processing and automation work to machines and have them carry out difficult tasks on our behalf. But programming computers is hard. The […]

ASSET Seminar: “Safety through Agility – Safe and Performant Control for Learning-Enabled Autonomous Systems” (Mangharam, Penn)

ABSTRACT: We present three approaches to combine formal methods, control theory, and machine learning for safe and performant autonomous systems.  Safe control for learning-enabled systems: We present our recent progress […]

ASSET Seminar: “The Road to Explainable AI v2.0” (Eric Wong, Penn)

ABSTRACT: “A.I. has an explainability crisis”—Fortune Magazine. If you ask an ML researcher about explainability, you’ll find that there are a large number of interpretability methods with no clear consensus […]

ASSET Seminar: “On Testing Properties of High-Dimensional Distributions” (Erik Waingarten, Penn)

ASSET Seminar: Machine Learning: A Data-Centric Perspective, Aleksander Madry (Massachusetts Institute of Technology)

ABSTRACT: The training data that modern machine learning models ingest has a major impact on these models’ performance (as well as failures). Yet, this impact tends to be neither fully […]

ASSET Seminar: Lockout: Sparse Regularization of Neural Networks, Gilmer Valdes (UCSF)

ABSTRACT: Many regression and classification procedures fit a function f(x;w) of predictor variables x to data 〖{x_i,y_i}〗_1^N based on some loss criterion L(y,f(x;w)). Often, regularization is applied to improve accuracy […]

ASSET Seminar: Neurosymbolic Programming for Science, Swarat Chaudhuri (University of Texas at Austin)

PRESENTATION ABSTRACT: Neurosymbolic programming (NSP) is an emerging area of computing that bridges the fields of deep learning and program synthesis. Like in classical machine learning, the goal here is to […]

ASSET Seminar: , Dinesh Jayaraman (University of Pennsylvania)

ABSTRACT: An important goal of the field sensorimotor robot learning is to do away with cumbersome expertise-intensive task specification, so that general-purpose robots of the future might learn large numbers […]

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 […]