Center for Trustworthy Machine Learning

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Background

Our Research

What We Do

Interdisciplinary Research

Student Summer Camps

High School Teacher Trainings

Outreach to Policy Makers

Annual Conference

NEWS

Annual Conference

IEEE Conference on Secure and Trustworthy Machine Learning (SaTML) 

New Data Sets

1. Measuring Massive Multitask Language Understanding

2. Natural Adversarial Examples

Most Recent Publications:

1. Data Poisoning Won’t Save You From Facial Recognition

2. On the Robustness of Domain Constraints

3. WILDS: A Benchmark of In-The-Wild Distribution Shifts

 

 

 

The Center for Trustworthy Machine Learning (CTML) is an Frontier in Secure & Trustworthy Computing, and it is supported by the National Science Foundation.

The focus of the Center is to develop a rigorous understanding of the vulnerabilities inherent to machine learning, and to develop the tools, metrics, and methods to mitigate them.

Picture of National Science Foundation's logo.
Support for the Center for Trustworthy Machine Learning (CTML) is provided through NSF Grant #(CNS-1805310), part of the NSF Secure and Trustworthy Cyberspace Program. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.
Additional support is provided byPenn State University,Stanford University,UC Berkeley,UC San Diego,University of Wisconsin,andUniversity of Virginia.