11 citations · 12 across the 3 of their papers we have counts for
4 papers
Multivariate conformal uncertainty propagation in multitask atomistic simulation: Successes and pitfalls
Katharine Fisher, Michael Herbst, James Kermode +1
Machine learning has become the standard tool for the design of interatomic potentials which balance efficiency and accuracy, but uncertainty quantification remains an open problem…
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…
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…
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…