4 citations · 4 across the 7 of their papers we have counts for
9 papers
Formal Bayesian Transfer Learning via the Total Risk Prior
Nathan Wycoff, Ali Arab, Lisa O. Singh
Existing methods for transfer learning struggle to deal with situations where the source datasets are limited and not guaranteed to be well-aligned with the target dataset. A typic…
Proximal Iteration for Nonlinear Adaptive Lasso
Nathan Wycoff, Lisa O. Singh, Ali Arab +1
Augmenting a smooth cost function with an penalty allows analysts to efficiently conduct estimation and variable selection simultaneously in sophisticated models and can b…
Regression Trees Know Calculus
Nathan Wycoff
Regression trees have emerged as a preeminent tool for solving real-world regression problems due to their ability to deal with nonlinearities, interaction effects and sharp discon…
Voronoi Candidates for Bayesian Optimization
Nathan Wycoff, John W. Smith, Annie S. Booth +1
Bayesian optimization (BO) offers an elegant approach for efficiently optimizing black-box functions. However, acquisition criteria demand their own challenging inner-optimization,…
Sparse Bayesian Lasso via a Variable-Coefficient Penalty
Nathan Wycoff, Ali Arab, Katharine M. Donato +1
Modern statistical learning algorithms are capable of amazing flexibility, but struggle with interpretability. One possible solution is sparsity: making inference such that many of…
Triangulation candidates for Bayesian optimization
Robert B. Gramacy, Annie Sauer, Nathan Wycoff
Bayesian optimization involves "inner optimization" over a new-data acquisition criterion which is non-convex/highly multi-modal, may be non-differentiable, or may otherwise thwart…
A survey on high-dimensional Gaussian process modeling with application to Bayesian optimization
Mickael Binois, Nathan Wycoff
Bayesian Optimization, the application of Bayesian function approximation to finding optima of expensive functions, has exploded in popularity in recent years. In particular, much…
Sensitivity Prewarping for Local Surrogate Modeling
Nathan Wycoff, Mickaël Binois, Robert B. Gramacy
In the continual effort to improve product quality and decrease operations costs, computational modeling is increasingly being deployed to determine feasibility of product designs…
Sequential Learning of Active Subspaces
Nathan Wycoff, Mickael Binois, Stefan M. Wild
In recent years, active subspace methods (ASMs) have become a popular means of performing subspace sensitivity analysis on black-box functions. Naively applied, however, ASMs requi…