Kilian Weinberger, "Interpretable Machine Learning"

Abstract:
Recent years have seen a revival of deep neural networks in machine learning. Although this has lead to impressive reduction in error rates in some prominent machine learning tasks, it also raises the concern about interpretability of machine learning algorithms.

In this talk I will describe the basics of deep learning algorithms and explain their basic building blocks. I will show that these are easy to understand. I will also try to shed some light onto what these networks learn on a higher level and hopefully be able to convince the audience that these are not “black box” algorithms, as they are often described …maybe instead gray or dark gray at most.

Bio:
Kilian Weinberger is an Associate Professor in the Department of
Computer Science at Cornell University. He received his Ph.D. from the University of Pennsylvania in Machine Learning under the supervision of Lawrence Saul and his undergraduate degree in Mathematics and Computer Science from the University of Oxford. During his career he has won several best paper awards at ICML, CVPR, AISTATS and KDD (runner-up award). In 2011 he was awarded the Outstanding AAAI Senior Program Chair Award and in 2012 he received an NSF CAREER award. He was elected
co-Program Chair for ICML 2016 and for AAAI 2018. Kilian Weinberger's research focuses on Machine Learning and its applications. In particular, he focuses on learning under resource constraints, metric learning, machine learned web-search ranking, computer vision and deep learning. Before joining Cornell University, he was an Associate Professor at Washington University in St. Louis and before that he worked as a research scientist at Yahoo! Research.