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Here are the videos of the Workshop on the Interface of Machine Learning and Statistical Inference
Organizers
- Giles Hooker (Cornell University)
- Gerard Biau (University Pierre and Marie Curie)
- Lucas Mentch (University of Pittsburgh)
- Stefan Wager (Stanford University)
Description
The Banff International Research Station will host the "Workshop on the Interface of Machine Learning and Statistical Inference" workshop from January 14th to January 19th, 2018.
Over the past thirty years, Machine Learning has proved enormously successful in using large databases to produce automatic prediction methods; they have been used in fields from handwriting recognition to automatic share market investments. However, these techniques produce little insight into the underlying mechanisms the result in the outcomes, nor do they provide statistical quantification of uncertainty. This workshop will bring together statisticians, mathematicians, and computer scientists to build on recent advances that seek to integrate machine learning with more traditional statistical models to obtain both highly accurate and understandable models while quantifying uncertainty about their predictions and conclusions.
- Rich Caruana, Microsoft Research, Friends Don’t Let Friends Deploy Black-Box Models: The Importance of Transparency and Intelligibility in Machine Learning Watch video | Download video: 201801150907-Caruana.mp4 (190M)
- Bin Yu, University of Berkeley, Stability and Iterative Random Forests Watch video | Download video: 201801151002-Yu.mp4 (159M)
- Lucas Mentch, University of Pittsburgh, Inference and Variable Selection for Random Forests
- Watch video | Download video: 201801151117-Mentch.mp4 (191M)
- Whitney Newey, Massachusetts Institute of Technology, Inference for Functionals of Machine Learning Estimators Watch video | Download video: 201801160911-Newey.mp4 (185M)
- Erwan Scornet, Ecole Polytechnique, Consistency of Random Forests Watch video | Download video: 201801161113-Scornet.mp4 (162M)
- Jelena Bradic, University of California - San Diego, High dimensional inference: do we need sparsity? Watch video | Download video: 201801161424-Bradic.mp4 (138M)
- Torsten Hothorn, University of Zurich, Transformation Forests Watch video | Download video: 201801161507-Hothorn.mp4 (128M)
- Adele Cutler, Utah State University, Random Forests - a Statistical Tool for the Sciences Watch video | Download video: 201801161643-Cutler.mp4 (205M)
- Edward George, University of Pennsylvania, The Remarkable Flexibility of BART Watch video | Download video: 201801170909-George.mp4 (162M)
- Andrew Wilson, Cornell University, Bayesian GANs and Stochastic MCMC Watch video | Download video: 201801171001-Wilson.mp4 (169M)
- Lucas Janson, Harvard University, Knockoffs: using machine learning for finite-sample controlled variable selection in nonparametric models Watch video | Download video: 201801171110-Janson.mp4 (174M)
- Susan Athey, Stanford, SHOPPER: A PROBABILISTIC MODEL OF CONSUMER CHOICE WITH SUBSTITUTES AND COMPLEMENTS Watch video | Download video: 201801180906-Athey.mp4 (164M)
- Jennifer Hill, New York University, Causal inferences that capitalizes on machine learning and statistics: opportunities and challenges Watch video | Download video: 201801180952-Hill.mp4 (149M)
- Mark van der Laan, University of California Berkeley, Targeted Learning: Integrating the State of the Art of Machine Learning with Statistical Inference Watch video | Download video: 201801181101-vanderLaan.mp4 (162M)
- Nathan Kallus, Cornell University, Generalized Optimal Matching for Inference and Policy Learning Watch video | Download video: 201801181511-Kallus.mp4 (97M)
- Ashkan Ertefaie, University of Rochester ; A Greedy Gradient Q-learning Approach for Constructing Optimal Policies in Infinite Time Horizon Settings Watch video | Download video: 201801181643-Ertefaie.mp4 (99M)
- Alexandra Chouldechova, CMU, "Algorithmic bias": Practical and technical challenges Watch video | Download video: 201801181717-Chouldechova.mp4 (115M)
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