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Signal Enhancement for Magnetic Navigation Challenge Problem


Jul 23, 2020
Albert R. Gnadt, Joseph Belarge, Aaron Canciani, Lauren Conger, Joseph Curro, Alan Edelman, Peter Morales, Michael F. O'Keeffe, Jonathan Taylor, Christopher Rackauckas

* 21 pages, 4 figures. See https://github.com/MIT-AI-Accelerator/MagNav.jl for accompanying data and code 

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Deep Implicit Volume Compression


May 18, 2020
Danhang Tang, Saurabh Singh, Philip A. Chou, Christian Haene, Mingsong Dou, Sean Fanello, Jonathan Taylor, Philip Davidson, Onur G. Guleryuz, Yinda Zhang, Shahram Izadi, Andrea Tagliasacchi, Sofien Bouaziz, Cem Keskin

* Danhang Tang and Saurabh Singh have equal contribution 

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Volumetric Capture of Humans with a Single RGBD Camera via Semi-Parametric Learning


May 29, 2019
Rohit Pandey, Anastasia Tkach, Shuoran Yang, Pavel Pidlypenskyi, Jonathan Taylor, Ricardo Martin-Brualla, Andrea Tagliasacchi, George Papandreou, Philip Davidson, Cem Keskin, Shahram Izadi, Sean Fanello


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LookinGood: Enhancing Performance Capture with Real-time Neural Re-Rendering


Nov 12, 2018
Ricardo Martin-Brualla, Rohit Pandey, Shuoran Yang, Pavel Pidlypenskyi, Jonathan Taylor, Julien Valentin, Sameh Khamis, Philip Davidson, Anastasia Tkach, Peter Lincoln, Adarsh Kowdle, Christoph Rhemann, Dan B Goldman, Cem Keskin, Steve Seitz, Shahram Izadi, Sean Fanello

* The supplementary video is available at: http://youtu.be/Md3tdAKoLGU To be presented at SIGGRAPH Asia 2018 

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Why Adaptively Collected Data Have Negative Bias and How to Correct for It


Dec 30, 2017
Xinkun Nie, Xiaoying Tian, Jonathan Taylor, James Zou

* Accepted to the 21st International Conference on Artificial Intelligence and Statistics (AISTATS) 2018, Lanzarote, Spain 

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Summary - TerpreT: A Probabilistic Programming Language for Program Induction


Dec 02, 2016
Alexander L. Gaunt, Marc Brockschmidt, Rishabh Singh, Nate Kushman, Pushmeet Kohli, Jonathan Taylor, Daniel Tarlow

* 7 pages, 2 figures, 4 tables in 1st Workshop on Neural Abstract Machines & Program Induction (NAMPI), @NIPS 2016 

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High-dimensional regression adjustments in randomized experiments


Oct 27, 2016
Stefan Wager, Wenfei Du, Jonathan Taylor, Robert Tibshirani

* To appear in the Proceedings of the National Academy of Sciences. The present draft does not reflect final copyediting by the PNAS staff 

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TerpreT: A Probabilistic Programming Language for Program Induction


Aug 15, 2016
Alexander L. Gaunt, Marc Brockschmidt, Rishabh Singh, Nate Kushman, Pushmeet Kohli, Jonathan Taylor, Daniel Tarlow

* 50 pages, 20 figures, 4 tables 

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Selective Sequential Model Selection


Dec 08, 2015
William Fithian, Jonathan Taylor, Robert Tibshirani, Ryan Tibshirani


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A lasso for hierarchical interactions


Jun 19, 2013
Jacob Bien, Jonathan Taylor, Robert Tibshirani

* Annals of Statistics 2013, Vol. 41, No. 3, 1111-1141 
* Published in at http://dx.doi.org/10.1214/13-AOS1096 the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org

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A Generalized Least Squares Matrix Decomposition


Mar 13, 2012
Genevera I. Allen, Logan Grosenick, Jonathan Taylor


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Strong rules for discarding predictors in lasso-type problems


Nov 24, 2010
Robert Tibshirani, Jacob Bien, Jerome Friedman, Trevor Hastie, Noah Simon, Jonathan Taylor, Ryan J. Tibshirani

* 5 

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