Interpreting Deep Neural Networks towards Trustworthiness

Followed by a panel discussion with:

Heather Harrington, University of Oxford- Mathematical Institute 

Chris Holmes, University of Oxford - Statistics Department 

Simon Tavare, Columbia University - Department of Systems Biology

Recent deep learning models have achieved impressive predictive performance by learning complex functions of many variables, often at the cost of interpretability. This lecture first defines interpretable machine learning in general and introduces the agglomerative contextual decomposition (ACD) method to interpret neural networks. Extending ACD to the scientifically meaningful frequency domain, an adaptive wavelet distillation (AWD) interpretation method is developed. AWD is shown to be both outperforming deep neural networks and interpretable in two prediction problems from cosmology and cell biology. Finally, a quality-controlled data science life cycle is advocated for building any model for trustworthy interpretation and introduce a Predictability Computability Stability (PCS) framework for such a data science life cycle.

Bin Yu is Chancellor's Distinguished Professor and Class of 1936 Second Chair in the departments of statistics and EECS at UC Berkeley. She leads the Yu Group which consists of students and postdocs from Statistics and EECS. Together with her group, her work has leveraged new computational developments to solve important scientific problems by combining novel statistical machine learning approaches with the domain expertise of her many collaborators in neuroscience, genomics, and precision medicine. She and her team develop relevant theories to understand random forests and deep learning for insight into and guidance for practice. She is a member of the U.S. National Academy of Sciences and of the American Academy of Arts and Sciences. She is Past President of the Institute of Mathematical Statistics (IMS), Guggenheim Fellow, Tukey Memorial Lecturer of the Bernoulli Society, Rietz Lecturer of IMS, and a COPSS E. L. Scott prize winner. She holds an Honorary Doctorate from The University of Lausanne (UNIL), Faculty of Business and Economics, in Switzerland. She has recently served on the inaugural scientific advisory committee of the UK Turing Institute for Data Science and AI, and is serving on the editorial board of Proceedings of the National Academy of Sciences (PNAS).