Seminar on Theoretical Machine Learning

Designing Fast and Robust Learning Algorithms

Yu Cheng
University of Illinois at Chicago
October 9, 2019

Most people interact with machine learning systems on a daily basis. Such interactions often happen in strategic environments where people have incentives to manipulate the learning algorithms. As machine learning plays a more prominent role in our society, it is important to understand whether existing algorithms are vulnerable to adversarial attacks and, if so, design new algorithms that are robust in these strategic environments. 

 

Unsupervised Ensemble Learning

Boaz Nadler
Weizmann Institute of Science; Member, School of Mathematics
October 8, 2019

In various applications, one is given the advice or predictions of several classifiers of unknown reliability, over multiple questions or queries. This scenario is different from standard supervised learning where classifier accuracy can be assessed from available labeled training or validation data, and raises several questions: given only the predictions of several classifiers of unknown accuracies, over a large set of unlabeled test data, is it possible to

a) reliably rank them, and