509 citations · 3.2k across the 61 of their papers we have counts for
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Adaptive Sampling Quasi-Newton Methods for Derivative-Free Stochastic Optimization
Raghu Bollapragada, Stefan M. Wild
We consider stochastic zero-order optimization problems, which arise in settings from simulation optimization to reinforcement learning. We propose an adaptive sampling quasi-Newto…
Scalable Reinforcement-Learning-Based Neural Architecture Search for Cancer Deep Learning Research
Prasanna Balaprakash, Romain Egele, Misha Salim +5
Cancer is a complex disease, the understanding and treatment of which are being aided through increases in the volume of collected data and in the scale of deployed computing power…
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…
Derivative-free optimization methods
Jeffrey Larson, Matt Menickelly, Stefan M. Wild
In many optimization problems arising from scientific, engineering and artificial intelligence applications, objective and constraint functions are available only as the output of…
A Method for Convex Black-Box Integer Global Optimization
Jeffrey Larson, Sven Leyffer, Prashant Palkar +1
We study the problem of minimizing a convex function on a nonempty, finite subset of the integer lattice when the function cannot be evaluated at noninteger points. We propose a ne…
Simultaneous Sensing Error Recovery and Tomographic Inversion Using an Optimization-based Approach
Anthony P. Austin, Zichao Wendy Di, Sven Leyffer +1
Tomography can be used to reveal internal properties of a 3D object using any penetrating wave. Advanced tomographic imaging techniques, however, are vulnerable to both systematic…