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08/24/2023
Don't blame Dataset Shift! Shortcut Learning due to Gradients and Cross Entropy
Common explanations for shortcut learning assume that the shortcut impro...
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08/08/2023
When More is Less: Incorporating Additional Datasets Can Hurt Performance By Introducing Spurious Correlations
In machine learning, incorporating more data is often seen as a reliable...
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03/22/2023
A dynamic risk score for early prediction of cardiogenic shock using machine learning
Myocardial infarction and heart failure are major cardiovascular disease...
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02/24/2023
Don't be fooled: label leakage in explanation methods and the importance of their quantitative evaluation
Feature attribution methods identify which features of an input most inf...
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02/14/2023
Where to Diffuse, How to Diffuse, and How to Get Back: Automated Learning for Multivariate Diffusions
Diffusion-based generative models (DBGMs) perturb data to a target noise...
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02/08/2023
Robustness to Spurious Correlations Improves Semantic Out-of-Distribution Detection
Methods which utilize the outputs or feature representations of predicti...
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01/27/2023
On the Feasibility of Machine Learning Augmented Magnetic Resonance for Point-of-Care Identification of Disease
Early detection of many life-threatening diseases (e.g., prostate and br...
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10/04/2022
Nuisances via Negativa: Adjusting for Spurious Correlations via Data Augmentation
There exist features that are related to the label in the same way acros...
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09/15/2022
Decision making in cancer: Causal questions require causal answers
Treatment decisions in cancer care are guided by treatment effect estima...
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08/23/2022
Survival Mixture Density Networks
Survival analysis, the art of time-to-event modeling, plays an important...
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08/18/2022
DIET: Conditional independence testing with marginal dependence measures of residual information
Conditional randomization tests (CRTs) assess whether a variable x is pr...
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06/23/2022
Set Norm and Equivariant Skip Connections: Putting the Deep in Deep Sets
Permutation invariant neural networks are a promising tool for making pr...
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04/29/2022
Conditional average treatment effect estimation with treatment offset models
Treatment effect estimates are often available from randomized controlle...
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12/02/2021
Quantile Filtered Imitation Learning
We introduce quantile filtered imitation learning (QFIL), a novel policy...
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12/01/2021
Learning Invariant Representations with Missing Data
Spurious correlations allow flexible models to predict well during train...
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11/16/2021
Inverse-Weighted Survival Games
Deep models trained through maximum likelihood have achieved state-of-th...
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07/15/2021
FastSHAP: Real-Time Shapley Value Estimation
Shapley values are widely used to explain black-box models, but they are...
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07/14/2021
Understanding Failures in Out-of-Distribution Detection with Deep Generative Models
Deep generative models (DGMs) seem a natural fit for detecting out-of-di...
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06/29/2021
Predictive Modeling in the Presence of Nuisance-Induced Spurious Correlations
Deep predictive models often make use of spurious correlations between t...
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06/16/2021
Offline RL Without Off-Policy Evaluation
Most prior approaches to offline reinforcement learning (RL) have taken ...
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03/02/2021
Have We Learned to Explain?: How Interpretability Methods Can Learn to Encode Predictions in their Interpretations
While the need for interpretable machine learning has been established, ...
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02/17/2021
Causal Estimation with Functional Confounders
Causal inference relies on two fundamental assumptions: ignorability and...
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01/13/2021
X-CAL: Explicit Calibration for Survival Analysis
Survival analysis models the distribution of time until an event of inte...
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09/23/2020
Probabilistic Machine Learning for Healthcare
Machine learning can be used to make sense of healthcare data. Probabili...
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07/31/2020
Deep Direct Likelihood Knockoffs
Predictive modeling often uses black box machine learning methods, such ...
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06/27/2020
Overfitting and Optimization in Offline Policy Learning
We consider the task of policy learning from an offline dataset generate...
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01/09/2020
The Counterfactual χ-GAN
Causal inference often relies on the counterfactual framework, which req...
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10/31/2019
Energy-Inspired Models: Learning with Sampler-Induced Distributions
Energy-based models (EBMs) are powerful probabilistic models, but suffer...
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08/02/2019
Population Predictive Checks
Bayesian modeling has become a staple for researchers analyzing data. Th...
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07/08/2019
Generalized Control Functions via Variational Decoupling
Causal estimation relies on separating the variation in the outcome due ...
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07/02/2019
Reproducibility in Machine Learning for Health
Machine learning algorithms designed to characterize, monitor, and inter...
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05/13/2019
Adversarial Examples for Electrocardiograms
Among all physiological signals, electrocardiogram (ECG) has seen some o...
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04/10/2019
ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission
Clinical notes contain information about patients that goes beyond struc...
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04/09/2019
Kernelized Complete Conditional Stein Discrepancy
Much of machine learning relies on comparing distributions with discrepa...
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03/25/2019
The Random Conditional Distribution for Higher-Order Probabilistic Inference
The need to condition distributional properties such as expectation, var...
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03/08/2019
Support and Invertibility in Domain-Invariant Representations
Learning domain-invariant representations has become a popular approach ...
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03/07/2019
The Variational Predictive Natural Gradient
Variational inference transforms posterior inference into parametric opt...
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01/16/2019
Soft Constraints for Inference with Declarative Knowledge
We develop a likelihood free inference procedure for conditioning a prob...
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06/01/2018
Opportunities in Machine Learning for Healthcare
Healthcare is a natural arena for the application of machine learning, e...
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05/21/2018
Multiple Causal Inference with Latent Confounding
Causal inference from observational data requires assumptions. These ass...
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05/03/2018
Noisin: Unbiased Regularization for Recurrent Neural Networks
Recurrent neural networks (RNNs) are powerful models of sequential data....
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05/31/2017
Variational Sequential Monte Carlo
Variational inference underlies many recent advances in large scale prob...
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05/24/2017
Proximity Variational Inference
Variational inference is a powerful approach for approximate posterior i...
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02/28/2017
Hierarchical Implicit Models and Likelihood-Free Variational Inference
Implicit probabilistic models are a flexible class of models defined by ...
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11/01/2016
Variational Inference via χ-Upper Bound Minimization
Variational inference (VI) is widely used as an efficient alternative to...
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10/27/2016
Operator Variational Inference
Variational inference is an umbrella term for algorithms which cast Baye...
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08/06/2016
Deep Survival Analysis
The electronic health record (EHR) provides an unprecedented opportunity...
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03/02/2016
Automatic Differentiation Variational Inference
Probabilistic modeling is iterative. A scientist posits a simple model, ...
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11/20/2015
The Variational Gaussian Process
Variational inference is a powerful tool for approximate inference, and ...
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11/07/2015
