Daniel Hendrycks

EECS Department
University of California, Berkeley
Technical Report No. UCB/EECS-2022-133
May 16, 2022

http://www2.eecs.berkeley.edu/Pubs/TechRpts/2022/EECS-2022-133.pdf

Machine learning (ML) systems are rapidly increasing in size, are acquiring new capabilities, and are increasingly deployed in high-stakes settings. To address the growing need for safe ML systems, I first discuss works towards making systems perform reliably. Thereafter I discuss works towards making systems act in accordance with human values. In closing I discuss open problems in making ML systems safer.

Advisor: Dawn Song and Jacob Steinhardt

BibTeX citation:

@phdthesis{Hendrycks:EECS-2022-133,
    Author = {Hendrycks, Daniel},
    Title = {Machine Learning Safety},
    School = {EECS Department, University of California, Berkeley},
    Year = {2022},
    Month = {May},
    URL = {http://www2.eecs.berkeley.edu/Pubs/TechRpts/2022/EECS-2022-133.html},
    Number = {UCB/EECS-2022-133},
    Abstract = {Machine learning (ML) systems are rapidly increasing in size, are acquiring new capabilities, and are increasingly deployed in high-stakes settings. To address the growing need for safe ML systems, I first discuss works towards making systems perform reliably. Thereafter I discuss works towards making systems act in accordance with human values. In closing I discuss open problems in making ML systems safer.}
}

EndNote citation:

%0 Thesis
%A Hendrycks, Daniel
%T Machine Learning Safety
%I EECS Department, University of California, Berkeley
%D 2022
%8 May 16
%@ UCB/EECS-2022-133
%U http://www2.eecs.berkeley.edu/Pubs/TechRpts/2022/EECS-2022-133.html
%F Hendrycks:EECS-2022-133