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cs.LG2026
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…
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…