Anna Goldenberg

research

∙ 05/06/2023

Maintaining Stability and Plasticity for Predictive Churn Reduction

Deployed machine learning models should be updated to take advantage of ...

0 George Adam, et al. ∙

research

∙ 12/27/2022

From Single-Visit to Multi-Visit Image-Based Models: Single-Visit Models are Enough to Predict Obstructive Hydronephrosis

Previous work has shown the potential of deep learning to predict renal ...

0 Stanley Bryan Z. Hua, et al. ∙

research

∙ 11/08/2022

Time-Varying Correlation Networks for Interpretable Change Point Detection

Change point detection (CPD) methods aim to detect abrupt changes in tim...

0 Kopal Garg, et al. ∙

research

∙ 10/21/2022

Considerations for Visualizing Uncertainty in Clinical Machine Learning Models

Clinician-facing predictive models are increasingly present in the healt...

0 Caitlin F. Harrigan, et al. ∙

research

∙ 02/04/2022

Decoupling Local and Global Representations of Time Series

Real-world time series data are often generated from several sources of ...

0 Sana Tonekaboni, et al. ∙

research

∙ 10/28/2021

Extracting Clinician's Goals by What-if Interpretable Modeling

Although reinforcement learning (RL) has tremendous success in many fiel...

0 Chun-Hao Chang, et al. ∙

research

∙ 06/03/2021

NODE-GAM: Neural Generalized Additive Model for Interpretable Deep Learning

Deployment of machine learning models in real high-risk settings (e.g. h...

0 Chun-Hao Chang, et al. ∙

research

∙ 06/02/2021

Towards Robust Classification Model by Counterfactual and Invariant Data Generation

Despite the success of machine learning applications in science, industr...

0 Chun-Hao Chang, et al. ∙

research

∙ 06/01/2021

Unsupervised Representation Learning for Time Series with Temporal Neighborhood Coding

Time series are often complex and rich in information but sparsely label...

0 Sana Tonekaboni, et al. ∙

research

∙ 03/24/2021

3D Reasoning for Unsupervised Anomaly Detection in Pediatric WbMRI

Modern deep unsupervised learning methods have shown great promise for d...

3 Alex Chang, et al. ∙

research

∙ 11/09/2020

Forecasting Emergency Department Capacity Constraints for COVID Isolation Beds

Predicting patient volumes in a hospital setting is a well-studied appli...

0 Erik Drysdale, et al. ∙

research

∙ 10/13/2020

Chasing Your Long Tails: Differentially Private Prediction in Health Care Settings

Machine learning models in health care are often deployed in settings wh...

0 Vinith M. Suriyakumar, et al. ∙

research

∙ 07/20/2020

A Comprehensive Evaluation of Multi-task Learning and Multi-task Pre-training on EHR Time-series Data

Multi-task learning (MTL) is a machine learning technique aiming to impr...

10 Matthew B. A. McDermott, et al. ∙

research

∙ 06/11/2020

How Interpretable and Trustworthy are GAMs?

Generalized additive models (GAMs) have become a leading model class for...

0 Chun-Hao Chang, et al. ∙

research

∙ 06/01/2020

Using Generative Models for Pediatric wbMRI

Early detection of cancer is key to a good prognosis and requires freque...

15 Alex Chang, et al. ∙

research

∙ 03/05/2020

What went wrong and when? Instance-wise Feature Importance for Time-series Models

Multivariate time series models are poised to be used for decision suppo...

17 Sana Tonekaboni, et al. ∙

research

∙ 08/02/2019

Feature Robustness in Non-stationary Health Records: Caveats to Deployable Model Performance in Common Clinical Machine Learning Tasks

When training clinical prediction models from electronic health records ...

0 Bret Nestor, et al. ∙

research

∙ 05/13/2019

What Clinicians Want: Contextualizing Explainable Machine Learning for Clinical End Use

Translating machine learning (ML) models effectively to clinical practic...

0 Sana Tonekaboni, et al. ∙

research

∙ 04/16/2019

Reducing Adversarial Example Transferability Using Gradient Regularization

Deep learning algorithms have increasingly been shown to lack robustness...

0 George Adam, et al. ∙

research

∙ 03/29/2019

The False Positive Control Lasso

In high dimensional settings where a small number of regressors are expe...

0 Erik Drysdale, et al. ∙

research

∙ 01/24/2019

Dynamic Measurement Scheduling for Event Forecasting using Deep RL

Current clinical practice for monitoring patients' health follows either...

0 Chun-Hao Chang, et al. ∙

research

∙ 12/03/2018

Prediction of New Onset Diabetes after Liver Transplant

25 within the next 5 years. These thousands of individuals are at 2-fold...

0 Angeline Yasodhara, et al. ∙

research

∙ 12/01/2018

Dynamic Measurement Scheduling for Adverse Event Forecasting using Deep RL

Current clinical practice to monitor patients' health follows either reg...

0 Chun-Hao Chang, et al. ∙

research

∙ 11/30/2018

Rethinking clinical prediction: Why machine learning must consider year of care and feature aggregation

Machine learning for healthcare often trains models on de-identified dat...

0 Bret Nestor, et al. ∙

research

∙ 08/20/2018

Stochastic Combinatorial Ensembles for Defending Against Adversarial Examples

Many deep learning algorithms can be easily fooled with simple adversari...

0 George A. Adam, et al. ∙

research

∙ 07/20/2018

Explaining Image Classifiers by Adaptive Dropout and Generative In-filling

Explanations of black-box classifiers often rely on saliency maps, which...

0 Chun-Hao Chang, et al. ∙

research

∙ 06/30/2018

Machine Learning for Integrating Data in Biology and Medicine: Principles, Practice, and Opportunities

New technologies have enabled the investigation of biology and human hea...

0 Marinka Zitnik, et al. ∙

research

∙ 12/22/2017

Dropout Feature Ranking for Deep Learning Models

Deep neural networks are a promising technology achieving state-of-the-a...

0 Chun-Hao Chang, et al. ∙

research

∙ 06/26/2017

Dr.VAE: Drug Response Variational Autoencoder

We present two deep generative models based on Variational Autoencoders ...

0 Ladislav Rampasek, et al. ∙

research

∙ 12/29/2009

A survey of statistical network models

Networks are ubiquitous in science and have become a focal point for dis...

0 Anna Goldenberg, et al. ∙

Anna Goldenberg

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