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05/06/2023
Maintaining Stability and Plasticity for Predictive Churn Reduction
Deployed machine learning models should be updated to take advantage of ...
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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 ...
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11/08/2022
Time-Varying Correlation Networks for Interpretable Change Point Detection
Change point detection (CPD) methods aim to detect abrupt changes in tim...
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10/21/2022
Considerations for Visualizing Uncertainty in Clinical Machine Learning Models
Clinician-facing predictive models are increasingly present in the healt...
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02/04/2022
Decoupling Local and Global Representations of Time Series
Real-world time series data are often generated from several sources of ...
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10/28/2021
Extracting Clinician's Goals by What-if Interpretable Modeling
Although reinforcement learning (RL) has tremendous success in many fiel...
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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...
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06/02/2021
Towards Robust Classification Model by Counterfactual and Invariant Data Generation
Despite the success of machine learning applications in science, industr...
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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...
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03/24/2021
3D Reasoning for Unsupervised Anomaly Detection in Pediatric WbMRI
Modern deep unsupervised learning methods have shown great promise for d...
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11/09/2020
Forecasting Emergency Department Capacity Constraints for COVID Isolation Beds
Predicting patient volumes in a hospital setting is a well-studied appli...
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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...
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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...
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06/11/2020
How Interpretable and Trustworthy are GAMs?
Generalized additive models (GAMs) have become a leading model class for...
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06/01/2020
Using Generative Models for Pediatric wbMRI
Early detection of cancer is key to a good prognosis and requires freque...
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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...
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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 ...
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05/13/2019
What Clinicians Want: Contextualizing Explainable Machine Learning for Clinical End Use
Translating machine learning (ML) models effectively to clinical practic...
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04/16/2019
Reducing Adversarial Example Transferability Using Gradient Regularization
Deep learning algorithms have increasingly been shown to lack robustness...
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03/29/2019
The False Positive Control Lasso
In high dimensional settings where a small number of regressors are expe...
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01/24/2019
Dynamic Measurement Scheduling for Event Forecasting using Deep RL
Current clinical practice for monitoring patients' health follows either...
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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...
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12/01/2018
Dynamic Measurement Scheduling for Adverse Event Forecasting using Deep RL
Current clinical practice to monitor patients' health follows either reg...
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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...
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08/20/2018
Stochastic Combinatorial Ensembles for Defending Against Adversarial Examples
Many deep learning algorithms can be easily fooled with simple adversari...
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07/20/2018
Explaining Image Classifiers by Adaptive Dropout and Generative In-filling
Explanations of black-box classifiers often rely on saliency maps, which...
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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...
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12/22/2017
Dropout Feature Ranking for Deep Learning Models
Deep neural networks are a promising technology achieving state-of-the-a...
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06/26/2017
Dr.VAE: Drug Response Variational Autoencoder
We present two deep generative models based on Variational Autoencoders ...
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12/29/2009