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Found 7 papers, 3 papers with code
Model-Agnostic Feature Selection with Additional Mutual Information
We show that f-divergences provide a broad class of proper test statistics.
Deep Direct Likelihood Knockoffs
Predictive modeling often uses black box machine learning methods, such as deep neural networks, to achieve state-of-the-art performance.
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, many common approaches are slow, lack fidelity, or hard to evaluate.
FastSHAP: Real-Time Shapley Value Estimation
Shapley values are widely used to explain black-box models, but they are costly to calculate because they require many model evaluations.
DIET: Conditional independence testing with marginal dependence measures of residual information
DIET tests the marginal independence of two random variables: $F(x \mid z)$ and $F(y \mid z)$ where $F(\cdot \mid z)$ is a conditional cumulative distribution function (CDF).
On the Feasibility of Machine Learning Augmented Magnetic Resonance for Point-of-Care Identification of Disease
We hypothesise that the disease classification task can be solved using a very small tailored subset of k-space data, compared to image reconstruction.
A dynamic risk score for early prediction of cardiogenic shock using machine learning
We developed a deep learning-based risk stratification tool, called CShock, for patients admitted into the cardiac ICU with acute decompensated heart failure and/or myocardial infarction to predict onset of cardiogenic shock.
