ASSET Seminar: “Towards a Design Flow for Verified AI-Based Autonomy” (Sajit A. Seshia, University of California, Berkeley)

ABSTRACT: Verified artificial intelligence (AI) is the goal of designing AI-based systems that have strong, ideally provable, assurances of correctness with respect to formally specified requirements. This talk will review […]

ASSET Seminar: “Lifelong Learning for Autonomous Systems: Progress and Challenges” (Eric Eaton, Penn)

ABSTRACT:  Research in lifelong or continual machine learning has advanced rapidly over the past few years, primarily focusing on enabling learned models to acquire new tasks over time while avoiding […]

ASSET Seminar: “Towards Code-Aware Code Models” (Baishakhi Ray, Columbia University)

ABSTRACT: The past decade has seen unprecedented growth in Software Engineering— developers spend enormous time and effort to create new products. With such enormous growth comes the responsibility of producing […]

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 […]