Robust and Equitable Uncertainty Estimation

Machine learning provides us with an amazing set of tools to make predictions, but how much should we trust particular predictions? To answer this, we need a way of estimating the confidence we should have in particular predictions of black-box models. Standard tools for doing this give guarantees that are averages over predictions. For instance, in a medical application, such tools might paper over poor performance on one medically relevant demographic group if it is made up for by higher performance on another group. Standard methods also depend on the data distribution being static — in other words, the future should be like the past.

In this lecture, Aaron Roth will describe a new technique to address both these problems: a way to produce prediction sets for arbitrary black-box prediction methods that have correct empirical coverage even when the data distribution might change in arbitrary, unanticipated ways and such that we have correct coverage even when we zoom in to focus on demographic groups that can be arbitrary and intersecting.

To attend this in-person event, you will need to register in advance and provide:

● Acceptable proof of vaccination (vaccine card/certificate, a copy or photo of vaccine card/certificate or electronic NYS Excelsior Pass or NJ Docket Pass)
● Photo ID
● Eventbrite ticket confirmation email with QR code
● Simons Foundation Health Screening Questionnaire approval email

Guests are expected to complete these requirements each time they visit the Simons Foundation and entrance will not be granted without this documentation.

On-site registration will not be permitted. Walk-in entry will be denied.