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09/07/2023
M(otion)-mode Based Prediction of Ejection Fraction using Echocardiograms
Early detection of cardiac dysfunction through routine screening is vita...
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06/19/2023
Beyond Normal: On the Evaluation of Mutual Information Estimators
Mutual information is a general statistical dependency measure which has...
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06/04/2023
(Un)reasonable Allure of Ante-hoc Interpretability for High-stakes Domains: Transparency Is Necessary but Insufficient for Explainability
Ante-hoc interpretability has become the holy grail of explainable machi...
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05/31/2023
Signal Is Harder To Learn Than Bias: Debiasing with Focal Loss
Spurious correlations are everywhere. While humans often do not perceive...
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05/26/2023
Differentiable Random Partition Models
Partitioning a set of elements into an unknown number of mutually exclus...
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03/16/2023
Identifiability Results for Multimodal Contrastive Learning
Contrastive learning is a cornerstone underlying recent progress in mult...
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02/28/2023
Interpretable and Intervenable Ultrasonography-based Machine Learning Models for Pediatric Appendicitis
Appendicitis is among the most frequent reasons for pediatric abdominal ...
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12/23/2022
Introduction to Machine Learning for Physicians: A Survival Guide for Data Deluge
Many modern research fields increasingly rely on collecting and analysin...
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10/13/2022
On the Identifiability and Estimation of Causal Location-Scale Noise Models
We study the class of location-scale or heteroscedastic noise models (LS...
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07/26/2022
Debiasing Deep Chest X-Ray Classifiers using Intra- and Post-processing Methods
Deep neural networks for image-based screening and computer-aided diagno...
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06/30/2022
Interpretable Anomaly Detection in Echocardiograms with Dynamic Variational Trajectory Models
We propose a novel anomaly detection method for echocardiogram videos. T...
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06/17/2022
How robust are pre-trained models to distribution shift?
The vulnerability of machine learning models to spurious correlations ha...
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03/03/2022
Learning Selection Bias and Group Importance: Differentiable Reparameterization for the Hypergeometric Distribution
Partitioning a set of elements into a given number of groups of a priori...
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10/08/2021
On the Limitations of Multimodal VAEs
Multimodal variational autoencoders (VAEs) have shown promise as efficie...
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06/11/2021
Deep Conditional Gaussian Mixture Model for Constrained Clustering
Constrained clustering has gained significant attention in the field of ...
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05/06/2021
Generalized Multimodal ELBO
Multiple data types naturally co-occur when describing real-world phenom...
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01/19/2021
Interpretable Models for Granger Causality Using Self-explaining Neural Networks
Exploratory analysis of time series data can yield a better understandin...
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12/03/2020
Interpretability and Explainability: A Machine Learning Zoo Mini-tour
In this review, we examine the problem of designing interpretable and ex...
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06/15/2020
Multimodal Generative Learning Utilizing Jensen-Shannon-Divergence
Learning from different data types is a long-standing goal in machine le...
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06/05/2020
Generation of Differentially Private Heterogeneous Electronic Health Records
Electronic Health Records (EHRs) are commonly used by the machine learni...
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04/29/2019
Unsupervised Extraction of Phenotypes from Cancer Clinical Notes for Association Studies
The recent adoption of Electronic Health Records (EHRs) by health care p...
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04/14/2015