Хайлтын үр дүнгүүд - Ranganath, Rajesh

General Control Functions for Causal Effect Estimation from Instrumental Variables -н Puli, Aahlad, Ranganath, Rajesh

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Deep direct likelihood knockoffs -н Sudarshan, Mukund, Tansey, Wesley, Ranganath, Rajesh

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Causal Estimation with Functional Confounders -н Puli, Aahlad, Perotte, Adler J., Ranganath, Rajesh

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Understanding Failures in Out-of-Distribution Detection with Deep Generative Models -н Zhang, Lily H., Goldstein, Mark, Ranganath, Rajesh

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Have We Learned to Explain?: How Interpretability Methods Can Learn to Encode Predictions in their Interpretations. -н Jethani, Neil, Sudarshan, Mukund, Aphinyanaphongs, Yindalon, Ranganath, Rajesh

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Risk prediction for chronic kidney disease progression using heterogeneous electronic health record data and time series analysis -н Perotte, Adler, Ranganath, Rajesh, Hirsch, Jamie S, Blei, David, Elhadad, Noémie

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Contra: Contrarian statistics for controlled variable selection -н Sudarshan, Mukund, Puli, Aahlad, Subramanian, Lakshmi, Sankararaman, Sriram, Ranganath, Rajesh

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Topographic Factor Analysis: A Bayesian Model for Inferring Brain Networks from Neural Data -н Manning, Jeremy R., Ranganath, Rajesh, Norman, Kenneth A., Blei, David M.

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X-CAL: Explicit Calibration for Survival Analysis -н Goldstein, Mark, Han, Xintian, Puli, Aahlad, Perotte, Adler J., Ranganath, Rajesh

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Deep learning models for electrocardiograms are susceptible to adversarial attack -н Han, Xintian, Hu, Yuxuan, Foschini, Luca, Chinitz, Larry, Jankelson, Lior, Ranganath, Rajesh

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Inverse-Weighted Survival Games -н Han, Xintian, Goldstein, Mark, Puli, Aahlad, Wies, Thomas, Perotte, Adler J., Ranganath, Rajesh

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A Review of Challenges and Opportunities in Machine Learning for Health -н Ghassemi, Marzyeh, Naumann, Tristan, Schulam, Peter, Beam, Andrew L., Chen, Irene Y., Ranganath, Rajesh

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Individual treatment effect estimation in the presence of unobserved confounding using proxies: a cohort study in stage III non-small cell lung cancer -н van Amsterdam, Wouter A. C., Verhoeff, Joost. J. C., Harlianto, Netanja I., Bartholomeus, Gijs A., Puli, Aahlad Manas, de Jong, Pim A., Leiner, Tim, van Lindert, Anne S. R., Eijkemans, Marinus J. C., Ranganath, Rajesh

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The role of machine learning in clinical research: transforming the future of evidence generation -н Weissler, E. Hope, Naumann, Tristan, Andersson, Tomas, Ranganath, Rajesh, Elemento, Olivier, Luo, Yuan, Freitag, Daniel F., Benoit, James, Hughes, Michael C., Khan, Faisal, Slater, Paul, Shameer, Khader, Roe, Matthew, Hutchison, Emmette, Kollins, Scott H., Broedl, Uli, Meng, Zhaoling, Wong, Jennifer L., Curtis, Lesley, Huang, Erich, Ghassemi, Marzyeh

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Correction to: The role of machine learning in clinical research: transforming the future of evidence generation -н Weissler, E. Hope, Naumann, Tristan, Andersson, Tomas, Ranganath, Rajesh, Elemento, Olivier, Luo, Yuan, Freitag, Daniel F., Benoit, James, Hughes, Michael C., Khan, Faisal, Slater, Paul, Shameer, Khader, Roe, Matthew, Hutchison, Emmette, Kollins, Scott H., Broedl, Uli, Meng, Zhaoling, Wong, Jennifer L., Curtis, Lesley, Huang, Erich, Ghassemi, Marzyeh

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A validated, real-time prediction model for favorable outcomes in hospitalized COVID-19 patients -н Razavian, Narges, Major, Vincent J., Sudarshan, Mukund, Burk-Rafel, Jesse, Stella, Peter, Randhawa, Hardev, Bilaloglu, Seda, Chen, Ji, Nguy, Vuthy, Wang, Walter, Zhang, Hao, Reinstein, Ilan, Kudlowitz, David, Zenger, Cameron, Cao, Meng, Zhang, Ruina, Dogra, Siddhant, Harish, Keerthi B., Bosworth, Brian, Francois, Fritz, Horwitz, Leora I., Ranganath, Rajesh, Austrian, Jonathan, Aphinyanaphongs, Yindalon

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