3 papers
math.FA2026
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…
cs.LG2026
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…
stat.CO2023
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…