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Three principles of data science: predictability, stability, and computability

Three principles of data science: predictability, stability, and computability
Monday, April 16, 2018 - 1:10pm
Bin Yu (Statistics and EECS, UC Berkeley)
Abstract / Description: 

In this talk, I'd like to discuss the intertwining importance and connections of three principles of data science in the title.

They will be demonstrated in the context of two collaborative projects in neuroscience and genomics, respectively. The first project in neuroscience uses transfer learning to integrate fitted convolutional neural networks (CNNs) on ImageNet with regression methods to provide predictive and stable characterizations of neurons from the challenging primary visual cortex V4. The second project proposes iterative random forests (iRF) as a stablized RF to seek predictable and interpretable high-order interactions among biomolecules.


Lunch is provided and will be served at 12:45 pm.
Please RSVP here by Friday, April 13

Organizers: Guido Imbens, Susan Athey, Mohsen Bayati, and Stefan Wager
Sponsored by: Institute for Research in the Social Sciences and Graduate School of Business