no code implementations • 12 Feb 2024 • Zongliang Ji, Anna Goldenberg, Rahul G. Krishnan
Scheduling laboratory tests for ICU patients presents a significant challenge.
no code implementations • 6 Feb 2024 • Sujay Nagaraj, Walter Gerych, Sana Tonekaboni, Anna Goldenberg, Berk Ustun, Thomas Hartvigsen
We first demonstrate the importance of modelling the temporal nature of the label noise function and how existing methods will consistently underperform.
1 code implementation • 15 Jan 2024 • Shihao Ma, Andy G. X. Zeng, Benjamin Haibe-Kains, Anna Goldenberg, John E Dick, Bo wang
High-throughput omics profiling advancements have greatly enhanced cancer patient stratification.
1 code implementation • 23 May 2023 • Oleksii Tsepa, Bohdan Naida, Anna Goldenberg, Bo wang
Drug synergy, characterized by the amplified combined effect of multiple drugs, is critically important for optimizing therapeutic outcomes.
no code implementations • 6 May 2023 • George Adam, Benjamin Haibe-Kains, Anna Goldenberg
Deployed machine learning models should be updated to take advantage of a larger sample size to improve performance, as more data is gathered over time.
1 code implementation • 27 Dec 2022 • Stanley Bryan Z. Hua, Mandy Rickard, John Weaver, Alice Xiang, Daniel Alvarez, Kyla N. Velear, Kunj Sheth, Gregory E. Tasian, Armando J. Lorenzo, Anna Goldenberg, Lauren Erdman
Previous work has shown the potential of deep learning to predict renal obstruction using kidney ultrasound images.
no code implementations • 8 Nov 2022 • Kopal Garg, Jennifer Yu, Tina Behrouzi, Sana Tonekaboni, Anna Goldenberg
Identifying change points (CPs) in a time series is crucial to guide better decision making across various fields like finance and healthcare and facilitating timely responses to potential risks or opportunities.
no code implementations • 21 Oct 2022 • Caitlin F. Harrigan, Gabriela Morgenshtern, Anna Goldenberg, Fanny Chevalier
Clinician-facing predictive models are increasingly present in the healthcare setting.
1 code implementation • 4 Feb 2022 • Sana Tonekaboni, Chun-Liang Li, Sercan Arik, Anna Goldenberg, Tomas Pfister
Learning representations that capture the factors contributing to this variability enables a better understanding of the data via its underlying generative process and improves performance on downstream machine learning tasks.
no code implementations • 28 Oct 2021 • Chun-Hao Chang, George Alexandru Adam, Rich Caruana, Anna Goldenberg
Although reinforcement learning (RL) has tremendous success in many fields, applying RL to real-world settings such as healthcare is challenging when the reward is hard to specify and no exploration is allowed.
2 code implementations • ICLR 2022 • Chun-Hao Chang, Rich Caruana, Anna Goldenberg
Deployment of machine learning models in real high-risk settings (e. g. healthcare) often depends not only on the model's accuracy but also on its fairness, robustness, and interpretability.
1 code implementation • CVPR 2021 • Chun-Hao Chang, George Alexandru Adam, Anna Goldenberg
Despite the success of machine learning applications in science, industry, and society in general, many approaches are known to be non-robust, often relying on spurious correlations to make predictions.
Ranked #1 on Out-of-Distribution Generalization on UrbanCars
2 code implementations • ICLR 2021 • Sana Tonekaboni, Danny Eytan, Anna Goldenberg
Time series are often complex and rich in information but sparsely labeled and therefore challenging to model.
no code implementations • 24 Mar 2021 • Alex Chang, Vinith Suriyakumar, Abhishek Moturu, James Tu, Nipaporn Tewattanarat, Sayali Joshi, Andrea Doria, Anna Goldenberg
Modern deep unsupervised learning methods have shown great promise for detecting diseases across a variety of medical imaging modalities.
no code implementations • NeurIPS 2020 • Sana Tonekaboni, Shalmali Joshi, Kieran Campbell, David K. Duvenaud, Anna Goldenberg
Explanations of time series models are useful for high stakes applications like healthcare but have received little attention in machine learning literature.
no code implementations • 9 Nov 2020 • Erik Drysdale, Devin Singh, Anna Goldenberg
Predicting patient volumes in a hospital setting is a well-studied application of time series forecasting.
no code implementations • 13 Oct 2020 • Vinith M. Suriyakumar, Nicolas Papernot, Anna Goldenberg, Marzyeh Ghassemi
Our results highlight lesser-known limitations of methods for DP learning in health care, models that exhibit steep tradeoffs between privacy and utility, and models whose predictions are disproportionately influenced by large demographic groups in the training data.
1 code implementation • 20 Jul 2020 • Matthew B. A. McDermott, Bret Nestor, Evan Kim, Wancong Zhang, Anna Goldenberg, Peter Szolovits, Marzyeh Ghassemi
Multi-task learning (MTL) is a machine learning technique aiming to improve model performance by leveraging information across many tasks.
2 code implementations • 11 Jun 2020 • Chun-Hao Chang, Sarah Tan, Ben Lengerich, Anna Goldenberg, Rich Caruana
Generalized additive models (GAMs) have become a leading modelclass for interpretable machine learning.
no code implementations • MIDL 2019 • Alex Chang, Vinith M. Suriyakumar, Abhishek Moturu, Nipaporn Tewattanarat, Andrea Doria, Anna Goldenberg
Early detection of cancer is key to a good prognosis and requires frequent testing, especially in pediatrics.
no code implementations • 5 Mar 2020 • Sana Tonekaboni, Shalmali Joshi, Kieran Campbell, David Duvenaud, Anna Goldenberg
Explanations of time series models are useful for high stakes applications like healthcare but have received little attention in machine learning literature.
no code implementations • 25 Sep 2019 • Sana Tonekaboni, Shalmali Joshi, David Duvenaud, Anna Goldenberg
We propose a method to automatically compute the importance of features at every observation in time series, by simulating counterfactual trajectories given previous observations.
1 code implementation • 2 Aug 2019 • Bret Nestor, Matthew B. A. McDermott, Willie Boag, Gabriela Berner, Tristan Naumann, Michael C. Hughes, Anna Goldenberg, Marzyeh Ghassemi
When training clinical prediction models from electronic health records (EHRs), a key concern should be a model's ability to sustain performance over time when deployed, even as care practices, database systems, and population demographics evolve.
no code implementations • 13 May 2019 • Sana Tonekaboni, Shalmali Joshi, Melissa D McCradden, Anna Goldenberg
Translating machine learning (ML) models effectively to clinical practice requires establishing clinicians' trust.
no code implementations • 16 Apr 2019 • George Adam, Petr Smirnov, Benjamin Haibe-Kains, Anna Goldenberg
We investigate the transferability of adversarial examples between models using the angle between the input-output Jacobians of different models.
2 code implementations • 29 Mar 2019 • Erik Drysdale, Yingwei Peng, Timothy P. Hanna, Paul Nguyen, Anna Goldenberg
In high dimensional settings where a small number of regressors are expected to be important, the Lasso estimator can be used to obtain a sparse solution vector with the expectation that most of the non-zero coefficients are associated with true signals.
1 code implementation • 24 Jan 2019 • Chun-Hao Chang, Mingjie Mai, Anna Goldenberg
We answer this question by deep reinforcement learning (RL) that jointly minimizes the measurement cost and maximizes predictive gain, by scheduling strategically-timed measurements.
no code implementations • 3 Dec 2018 • Angeline Yasodhara, Mamatha Bhat, Anna Goldenberg
Both patient's historical data and observations at the current visit are informative in predicting whether the patient will develop diabetes within the following year.
no code implementations • 1 Dec 2018 • Chun-Hao Chang, Mingjie Mai, Anna Goldenberg
We address the scheduling problem using deep reinforcement learning (RL) to achieve high predictive gain and low measurement cost, by scheduling fewer, but strategically timed tests.
no code implementations • 30 Nov 2018 • Bret Nestor, Matthew B. A. McDermott, Geeticka Chauhan, Tristan Naumann, Michael C. Hughes, Anna Goldenberg, Marzyeh Ghassemi
Machine learning for healthcare often trains models on de-identified datasets with randomly-shifted calendar dates, ignoring the fact that data were generated under hospital operation practices that change over time.
no code implementations • 20 Aug 2018 • George A. Adam, Petr Smirnov, David Duvenaud, Benjamin Haibe-Kains, Anna Goldenberg
Many deep learning algorithms can be easily fooled with simple adversarial examples.
1 code implementation • ICLR 2019 • Chun-Hao Chang, Elliot Creager, Anna Goldenberg, David Duvenaud
We can rephrase this question to ask: which parts of the image, if they were not seen by the classifier, would most change its decision?
no code implementations • 30 Jun 2018 • Marinka Zitnik, Francis Nguyen, Bo wang, Jure Leskovec, Anna Goldenberg, Michael M. Hoffman
In this Review, we describe the principles of data integration and discuss current methods and available implementations.
1 code implementation • 22 Dec 2017 • Chun-Hao Chang, Ladislav Rampasek, Anna Goldenberg
Deep neural networks (DNNs) achieve state-of-the-art results in a variety of domains.
no code implementations • 26 Jun 2017 • Ladislav Rampasek, Daniel Hidru, Petr Smirnov, Benjamin Haibe-Kains, Anna Goldenberg
We present two deep generative models based on Variational Autoencoders to improve the accuracy of drug response prediction.
no code implementations • 4 Dec 2016 • Lauren Erdman, Ekansh Sharma, Eva Unternahrer, Shantala Hari Dass, Kieran ODonnell, Sara Mostafavi, Rachel Edgar, Michael Kobor, Helene Gaudreau, Michael Meaney, Anna Goldenberg
More than two thirds of mental health problems have their onset during childhood or adolescence.
no code implementations • 14 Sep 2014 • Daniel Hidru, Anna Goldenberg
Nonnegative matrix factorization (NMF) methods have proved to be powerful across a wide range of real-world clustering applications.
no code implementations • 10 Apr 2014 • Bo Wang, Anna Goldenberg
With $\ell_1$ relaxation, GLFS can find a sparse subset of features that is relevant to the Laplacian manifolds.
no code implementations • 29 Dec 2009 • Anna Goldenberg, Alice X. Zheng, Stephen E. Fienberg, Edoardo M. Airoldi
Formal statistical models for the analysis of network data have emerged as a major topic of interest in diverse areas of study, and most of these involve a form of graphical representation.