74 citations · 201 across the 7 of their papers we have counts for
7 papers
Learning Active Subspaces and Discovering Important Features with Gaussian Radial Basis Functions Neural Networks
Danny D'Agostino, Ilija Ilievski, Christine Annette Shoemaker
Providing a model that achieves a strong predictive performance and is simultaneously interpretable by humans is one of the most difficult challenges in machine learning research d…
pySOT and POAP: An event-driven asynchronous framework for surrogate optimization
David Eriksson, David Bindel, Christine A. Shoemaker
This paper describes Plumbing for Optimization with Asynchronous Parallelism (POAP) and the Python Surrogate Optimization Toolbox (pySOT). POAP is an event-driven framework for bui…
Efficient Multi-Objective Optimization through Population-based Parallel Surrogate Search
Taimoor Akhtar, Christine A. Shoemaker
Multi-Objective Optimization (MOO) is very difficult for expensive functions because most current MOO methods rely on a large number of function evaluations to get an accurate solu…
Multilevel Monte Carlo in Approximate Bayesian Computation
Ajay Jasra, Seongil Jo, David Nott +2
In the following article we consider approximate Bayesian computation (ABC) inference. We introduce a method for numerically approximating ABC posteriors using the multilevel Monte…
Efficient Hyperparameter Optimization of Deep Learning Algorithms Using Deterministic RBF Surrogates
Ilija Ilievski, Taimoor Akhtar, Jiashi Feng +1
Automatically searching for optimal hyperparameter configurations is of crucial importance for applying deep learning algorithms in practice. Recently, Bayesian optimization has be…
A General Stochastic Algorithmic Framework for Minimizing Expensive Black Box Objective Functions Based on Surrogate Models and Sensitivity Analysis
Yilun Wang, Christine A. Shoemaker
We are focusing on bound constrained global optimization problems, whose objective functions are computationally expensive black-box functions and have multiple local minima. The r…