[The 28th TrustML Young Scientist Seminar]

The TrustML Young Scientist Seminars (TrustML YSS) started from January 28, 2022.

The TrustML YSS is a video series that features young scientists giving talks and discoveries in relation with Trustworthy Machine Learning.

Timetable for the TrustML YSS online seminars from Sep. to Oct. 2022.

For more information please see the following site.
TrustML YSS

This network is funded by RIKEN-AIP's subsidy and JST, ACT-X Grant Number JPMJAX21AF, Japan.

【The 28th Seminar】

Date and Time: September 1st 11:00 am - 12:00 pm(JST)

Venue: Zoom webinar

Language: English

Speaker: Yaodong Yu (University of California, Berkeley)
Title: Predicting Out-of-Distribution Error with the Projection Norm
Short Abstract
We propose a metric -- Projection Norm -- to predict a model's performance on out-of-distribution (OOD) data without access to ground truth labels. Projection Norm first uses model predictions to pseudo-label test samples and then trains a new model on the pseudo-labels. The more the new model's parameters differ from an in-distribution model, the greater the predicted OOD error. Empirically, our approach outperforms existing methods on both image and text classification tasks and across different network architectures. Theoretically, we connect our approach to a bound on the test error for overparameterized linear models. Furthermore, we find that Projection Norm is the only approach that achieves non-trivial detection performance on adversarial examples. Our code is available at https://github.com/yaodongyu/ProjNorm.

This is a joint work with Zitong Yang, Alexander Wei, Yi Ma, and Jacob Steinhardt.

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