70 citations · 191 across the 13 of their papers we have counts for
10 papers · 1 filter
Have We Learned to Explain?: How Interpretability Methods Can Learn to Encode Predictions in their Interpretations
Neil Jethani, Mukund Sudarshan, Yindalon Aphinyanaphongs +1
While the need for interpretable machine learning has been established, many common approaches are slow, lack fidelity, or hard to evaluate. Amortized explanation methods reduce th…
Probabilistic Machine Learning for Healthcare
Irene Y. Chen, Shalmali Joshi, Marzyeh Ghassemi +1
Machine learning can be used to make sense of healthcare data. Probabilistic machine learning models help provide a complete picture of observed data in healthcare. In this review,…
Deep Direct Likelihood Knockoffs
Mukund Sudarshan, Wesley Tansey, Rajesh Ranganath
Predictive modeling often uses black box machine learning methods, such as deep neural networks, to achieve state-of-the-art performance. In scientific domains, the scientist often…
Kernelized Complete Conditional Stein Discrepancy
Raghav Singhal, Xintian Han, Saad Lahlou +1
Much of machine learning relies on comparing distributions with discrepancy measures. Stein's method creates discrepancy measures between two distributions that require only the un…
Support and Invertibility in Domain-Invariant Representations
Fredrik D. Johansson, David Sontag, Rajesh Ranganath
Learning domain-invariant representations has become a popular approach to unsupervised domain adaptation and is often justified by invoking a particular suite of theoretical resul…
Multiple Causal Inference with Latent Confounding
Rajesh Ranganath, Adler Perotte
Causal inference from observational data requires assumptions. These assumptions range from measuring confounders to identifying instruments. Traditionally, causal inference assump…