77 citations · 111 across the 14 of their papers we have counts for
17 papers
Adaptive Sampling Trust Region Optimization for Derivative-free Stochastic Functions and Deterministic Equality Constraints
Nicole Felice, Sara Shashaani, Lindon Roberts
We study optimization problems with noisy zeroth-order objective observations and deterministic nonlinear equality constraints with available derivatives. We propose a constrained…
Black-box Optimization Algorithms for Regularized Least-squares Problems
Yanjun Liu, Kevin H. Lam, Lindon Roberts
We consider the problem of optimizing the sum of a smooth, nonconvex function for which derivatives are unavailable, and a convex, nonsmooth function with easy-to-evaluate proximal…
Model Construction for Convex-Constrained Derivative-Free Optimization
Lindon Roberts
We develop a new approximation theory for linear and quadratic interpolation models, suitable for use in convex-constrained derivative-free optimization (DFO). Most existing model-…
Expected decrease for derivative-free algorithms using random subspaces
Warren Hare, Lindon Roberts, Clément W. Royer
Derivative-free algorithms seek the minimum of a given function based only on function values queried at appropriate points. Although these methods are widely used in practice, the…
An adaptively inexact first-order method for bilevel optimization with application to hyperparameter learning
Mohammad Sadegh Salehi, Subhadip Mukherjee, Lindon Roberts +1
Various tasks in data science are modeled utilizing the variational regularization approach, where manually selecting regularization parameters presents a challenge. The difficulty…
Analyzing Inexact Hypergradients for Bilevel Learning
Matthias J. Ehrhardt, Lindon Roberts
Estimating hyperparameters has been a long-standing problem in machine learning. We consider the case where the task at hand is modeled as the solution to an optimization problem.…