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Invited Talk
Machine Learning with Human Intelligence: Principled Corner Cutting (PC2)
Xiao-Li Meng

Tue Dec 07 02:00 PM -- 02:50 PM (PST) @ Regency Ballroom

With the ever increasing availability of quantitative information, especially data with complex spatial and/or temporal structures, two closely related fields are undergoing substantial evolution: Machine learning and Statistics. On a grand scale, both have the same goal: separating signal from noise. In terms of methodological choices, however, it is not uncommon to hear machine learners complain about statisticians’ excessive worrying over modeling and inferential principles to a degree of being willing to produce nothing, and to hear statisticians express discomfort with machine learners’ tendency to let ease of practical implementation trump principled justifications, to a point of being willing to deliver anything. To take advantage of the strengths of both fields, we need to train substantially more principled corner cutters. That is, we must train researchers who are at ease in formulating the solution from the soundest principles available, and equally at ease in cutting corners, guided by these principles, to retain as much statistical efficiency as feasible while maintaining algorithmic efficiency under time and resource constraints. This thinking process is demonstrated by applying the self-consistency principle (Efron, 1967; Lee, Li and Meng, 2010) to handling incomplete and/or irregularly spaced data with non-parametric and semi-parametric models, including signal processing via wavelets and sparsity estimation via the LASSO and related penalties.

Author Information

Xiao-Li Meng (Harvard University)

Xiao-Li Meng is the Whipple V. N. Jones Professor of Statistics and Chair of the Department of Statistics at Harvard University. He was the recipient of the 2001 COPSS (Committee of Presidents of Statistical Societies) Award for "The outstanding statistician under the age of forty", of the 2003 Distinguished Achievement Award and of the 2008 Distinguished Service Award from the International Chinese Statistics Association, and of the 1997-1998 University of Chicago Faculty Award for Excellence in Graduate Teaching. His degrees include B.S. (Fudan Mathematics, 1982), M.A. (Harvard Statistics, 1987), and Ph.D. (Harvard Statistics, 1990). He has served on editorial boards of The Annals of Statistics, Biometrika, Journal of The American Statistical Association, Bayesian Analysis and Bernoulli, as well as the co-editor of Statistica Sinica. He is an elected fellow of ASA and of IMS. His research interests include: Statistical inference with partially observed data and simulated data; Quantifying statistical information and efficiency; Statistical principles and foundational issues, such as multi-party inferences, the theory of ignorance, and the interplay between Bayesian and frequentist perspectives; Effective deterministic and stochastic algorithms for Bayesian and likelihood computation; Markov chain Monte Carlo; Multi-resolution modelling for signal and image data; Statistical issues in astronomy and astrophysics; Modelling and imputation in health and medical studies; Elegant mathematical statistics.