no code implementations • 13 Jul 2022 • Grant Duffy, Shoa L. Clarke, Matthew Christensen, Bryan He, Neal Yuan, Susan Cheng, David Ouyang
When predicting race, we show that tuning the proportion of a confounding variable (sex) in the training data significantly impacts model AUC (ranging from 0. 57 to 0. 84), while in training a sex prediction model, tuning a confounder (race) did not substantially change AUC (0. 81 - 0. 83).
no code implementations • 30 Apr 2022 • David Ouyang, John Theurer, Nathan R. Stein, J. Weston Hughes, Pierre Elias, Bryan He, Neal Yuan, Grant Duffy, Roopinder K. Sandhu, Joseph Ebinger, Patrick Botting, Melvin Jujjavarapu, Brian Claggett, James E. Tooley, Tim Poterucha, Jonathan H. Chen, Michael Nurok, Marco Perez, Adler Perotte, James Y. Zou, Nancy R. Cook, Sumeet S. Chugh, Susan Cheng, Christine M. Albert
The algorithm discriminates mortality with an AUC of 0. 83 (95% CI 0. 79-0. 87) surpassing the discrimination of the RCRI score with an AUC of 0. 67 (CI 0. 61-0. 72) in the held out test cohort.
no code implementations • 13 Oct 2021 • Bryan He, Matthew Thomson, Meena Subramaniam, Richard Perez, Chun Jimmie Ye, James Zou
Predicting phenotype from scRNA-seq is challenging for standard machine learning methods -- the number of cells measured can vary by orders of magnitude across individuals and the cell populations are also highly heterogeneous.
1 code implementation • 23 Jun 2021 • Grant Duffy, Paul P Cheng, Neal Yuan, Bryan He, Alan C. Kwan, Matthew J. Shun-Shin, Kevin M. Alexander, Joseph Ebinger, Matthew P. Lungren, Florian Rader, David H. Liang, Ingela Schnittger, Euan A. Ashley, James Y. Zou, Jignesh Patel, Ronald Witteles, Susan Cheng, David Ouyang
Left ventricular hypertrophy (LVH) results from chronic remodeling caused by a broad range of systemic and cardiovascular disease including hypertension, aortic stenosis, hypertrophic cardiomyopathy, and cardiac amyloidosis.
1 code implementation • 4 Sep 2019 • Bas Hofstra, Vivek V. Kulkarni, Sebastian Munoz-Najar Galvez, Bryan He, Dan Jurafsky, Daniel A. McFarland
Are underrepresented groups more likely to generate scientific innovations?
1 code implementation • 1 Nov 2018 • Bryan He, James Zou
In classification, the de facto method for aggregating individual losses is the average loss.
no code implementations • NeurIPS 2017 • Paroma Varma, Bryan He, Payal Bajaj, Imon Banerjee, Nishith Khandwala, Daniel L. Rubin, Christopher Ré
Obtaining enough labeled data to robustly train complex discriminative models is a major bottleneck in the machine learning pipeline.
2 code implementations • 10 Jul 2017 • Christopher De Sa, Bryan He, Ioannis Mitliagkas, Christopher Ré, Peng Xu
We propose a simple variant of the power iteration with an added momentum term, that achieves both the optimal sample and iteration complexity.
no code implementations • ICML 2017 • Stephen H. Bach, Bryan He, Alexander Ratner, Christopher Ré
Curating labeled training data has become the primary bottleneck in machine learning.
no code implementations • 25 Oct 2016 • Paroma Varma, Bryan He, Dan Iter, Peng Xu, Rose Yu, Christopher De Sa, Christopher Ré
Prior work has explored learning accuracies for these sources even without ground truth labels, but they assume that a single accuracy parameter is sufficient to model the behavior of these sources over the entire training set.
no code implementations • NeurIPS 2016 • Bryan He, Christopher De Sa, Ioannis Mitliagkas, Christopher Ré
Gibbs sampling is a Markov Chain Monte Carlo sampling technique that iteratively samples variables from their conditional distributions.