2.2k citations · 4.4k across the 41 of their papers we have counts for
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Multiplying Matrices Without Multiplying
Davis Blalock, John Guttag
Multiplying matrices is among the most fundamental and compute-intensive operations in machine learning. Consequently, there has been significant work on efficiently approximating…
Learning to Predict with Supporting Evidence: Applications to Clinical Risk Prediction
Aniruddh Raghu, John Guttag, Katherine Young +3
The impact of machine learning models on healthcare will depend on the degree of trust that healthcare professionals place in the predictions made by these models. In this paper, w…
Intuitively Assessing ML Model Reliability through Example-Based Explanations and Editing Model Inputs
Harini Suresh, Kathleen M. Lewis, John V. Guttag +1
Interpretability methods aim to help users build trust in and understand the capabilities of machine learning models. However, existing approaches often rely on abstract, complex v…
HyperMorph: Amortized Hyperparameter Learning for Image Registration
Andrew Hoopes, Malte Hoffmann, Bruce Fischl +2
We present HyperMorph, a learning-based strategy for deformable image registration that removes the need to tune important registration hyperparameters during training. Classical r…