1 citations · 1 across the 3 of their papers we have counts for
3 papers
cs.LG2024★ 1 cited
Leveraging viscous Hamilton-Jacobi PDEs for uncertainty quantification in scientific machine learning
Zongren Zou, Tingwei Meng, Paula Chen +2
Uncertainty quantification (UQ) in scientific machine learning (SciML) combines the powerful predictive power of SciML with methods for quantifying the reliability of the learned m…
stat.ML2024
Efficient first-order algorithms for large-scale, non-smooth maximum entropy models with application to wildfire science
Gabriel P. Langlois, Jatan Buch, Jérôme Darbon
Maximum entropy (Maxent) models are a class of statistical models that use the maximum entropy principle to estimate probability distributions from data. Due to the size of modern…
math.OC2021
Efficient and robust high-dimensional sparse logistic regression via nonlinear primal-dual hybrid gradient algorithms
Jérôme Darbon, Gabriel P. Langlois
Logistic regression is a widely used statistical model to describe the relationship between a binary response variable and predictor variables in data sets. It is often used in mac…