1 citations · 1 across the 2 of their papers we have counts for
3 papers
stat.ML2025
Precise asymptotic analysis of Sobolev training for random feature models
Katharine E Fisher, Matthew TC Li, Youssef Marzouk +1
Gradient information is widely useful and available in applications, and is therefore natural to include in the training of neural networks. Yet little is known theoretically about…
stat.ML2025★ 1 cited
Can Bayesian Neural Networks Make Confident Predictions?
Katharine Fisher, Youssef Marzouk
Bayesian inference promises a framework for principled uncertainty quantification of neural network predictions. Barriers to adoption include the difficulty of fully characterizing…
physics.chem-ph2024
Multitask methods for predicting molecular properties from heterogeneous data
Katharine Fisher, Michael Herbst, Youssef Marzouk
Data generation remains a bottleneck in training surrogate models to predict molecular properties. We demonstrate that multitask Gaussian process regression overcomes this limitati…