Workshop on the Interface of Machine Learning and Statistical Inference
Videos from BIRS Workshop
Rich Caruana, Microsoft Research
Monday Jan 15, 2018 09:07 - 10:00
Friends Don’t Let Friends Deploy Black-Box Models: The Importance of Transparency and Intelligibility in Machine Learning
Bin YU, UC Berkeley
Monday Jan 15, 2018 10:02 - 10:49
Stability and Iterative Random Forests
Lucas Mentch, University of Pittsburgh
Monday Jan 15, 2018 11:17 - 12:06
Inference and Variable Selection for Random Forests
Whitney Newey, Massachusetts Institute of Technology
Tuesday Jan 16, 2018 09:11 - 10:07
Inference for Functionals of Machine Learning Estimators
Erwan Scornet, Ecole Polytechnique
Tuesday Jan 16, 2018 11:13 - 12:02
Consistency of Random Forests
Jelena Bradic, University of California - San Diego
Tuesday Jan 16, 2018 14:24 - 15:06
High dimensional inference: do we need sparsity?
Torsten Hothorn, University of Zurich
Tuesday Jan 16, 2018 15:07 - 15:47
Transformation Forests
Adele Cutler, Utah State University
Tuesday Jan 16, 2018 16:43 - 17:23
Random Forests - a Statistical Tool for the Sciences
Edward George, University of Pennsylvania
Wednesday Jan 17, 2018 09:09 - 10:00
The Remarkable Flexibility of BART
Andrew Wilson, Cornell University
Wednesday Jan 17, 2018 10:01 - 10:51
Bayesian GANs and Stochastic MCMC
Lucas Janson, Harvard University
Wednesday Jan 17, 2018 11:10 - 12:06
Knockoffs: using machine learning for finite-sample controlled variable selection in nonparametric models
Susan Athey, Stanford
Thursday Jan 18, 2018 09:06 - 09:51
SHOPPER: A PROBABILISTIC MODEL OF CONSUMER CHOICE WITH SUBSTITUTES AND COMPLEMENTS
Jennifer Hill, New York University
Thursday Jan 18, 2018 09:52 - 10:38
Causal inferences that capitalizes on machine learning and statistics: opportunities and challenges
Mark van der Laan, University of California Berkeley
Thursday Jan 18, 2018 11:01 - 11:56
Targeted Learning: Integrating the State of the Art of Machine Learning with Statistical Inference
Nathan Kallus, Cornell University
Thursday Jan 18, 2018 15:11 - 15:43
Generalized Optimal Matching for Inference and Policy Learning
Ashkan Ertefaie, University of Rochester
Thursday Jan 18, 2018 16:43 - 17:14
A Greedy Gradient Q-learning Approach for Constructing Optimal Policies in Infinite Time Horizon Settings
Alexandra Chouldechova, CMU
Thursday Jan 18, 2018 17:17 - 17:50
"Algorithmic bias": Practical and technical challenges
