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Activity Number: 268 - A Unifying Theme for Interpretable Information Extraction from Data: The Stability Principle
Type: Invited
Date/Time: Tuesday, August 1, 2017 : 8:30 AM to 10:20 AM
Sponsor: IMS
Abstract #322080 View Presentation
Title: Three Principles for Data Science: Predictability, Stability and Computability
Author(s): Bin Yu*
Companies: University of California, Berkeley
Keywords: preditability ; stability ; data wisdom ; deep learning ; computability ; latent variable model
Abstract:

This talk discusses the intertwining importance and connections of three principles of data science in the title. The ultimate importance of prediction lies in the fact that future holds the unique and possibly the only purpose of all human activities, in business, education, research, and government alike. Making prediction as its central task and embracing computation as its core, machine learning has enabled wide-ranging data-driven successes. Prediction is a useful way to check with reality. Good prediction implicitly assumes stability between past and future. Stability (relative to data and model perturbations) is also a minimum requirement for interpretability and reproducibility of data driven results. It is closely related to uncertainty assessment. Obviously, both prediction and stability principles can not be employed without feasible computational algorithms, hence the importance of computability. The three principles will be demonstrated through analytical connections, and in the context of two on-going projects to understand neuron pattern selectivities through deep learning and comparing latent variable and Lasso-based predictive models in political science.


Authors who are presenting talks have a * after their name.

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