5 citations · 9 across the 4 of their papers we have counts for
5 papers
A Weighted Kernel Method for Approximation that Adapts to Learned Multivariable Structure
John E. Darges, Laura Weidensager
Approximating the input-output behavior of a multivariable black-box function from limited data is challenging when blind to the importance of its inputs and their interactions. We…
On high probability of universal approximation in random basis expansions with non-continuous weight sampling
John E. Darges
Random basis expansion (RBE) search the span of a randomly sampled basis to find the best approximation of a target function. They are equivalent to single layer neural networks wh…
Neural Optimal Design of Experiment for Inverse Problems
John E. Darges, Babak Maboudi Afkham, Matthias Chung
We introduce Neural Optimal Design of Experiments, a learning-based framework for optimal experimental design in inverse problems that avoids classical bilevel optimization and ind…
Variance-based sensitivity of Bayesian inverse problems to the prior distribution
John E. Darges, Alen Alexanderian, Pierre A. Gremaud
The formulation of Bayesian inverse problems involves choosing prior distributions; choices that seem equally reasonable may lead to significantly different conclusions. We develop…
Extreme learning machines for variance-based global sensitivity analysis
John Darges, Alen Alexanderian, Pierre Gremaud
Variance-based global sensitivity analysis (GSA) can provide a wealth of information when applied to complex models. A well-known Achilles' heel of this approach is its computation…