31 citations · 51 across the 15 of their papers we have counts for
18 papers
Gradient-Free Optimization for Matrix functions
Sawyer Allen, Cash Cherry, Aidan Eck +2
We consider the task of optimizing smooth, possibly non-convex functions of a matrix variable given access only to directional derivatives rather than full gradients. This setting…
Fully Adaptive Zeroth-Order Method for Minimizing Functions with Compressible Gradients
Geovani Nunes Grapiglia, Daniel McKenzie
We propose an adaptive zeroth-order method for minimizing differentiable functions with -Lipschitz continuous gradients. The method is designed to take advantage of the eventual…
Fermat Distances: Metric Approximation, Spectral Convergence, and Clustering Algorithms
Nicolás García Trillos, Anna Little, Daniel McKenzie +1
We analyze the convergence properties of Fermat distances, a family of density-driven metrics defined on Riemannian manifolds with an associated probability measure. Fermat distanc…
It begins with a boundary: A geometric view on probabilistically robust learning
Leon Bungert, Nicolás García Trillos, Matt Jacobs +3
Although deep neural networks have achieved super-human performance on many classification tasks, they often exhibit a worrying lack of robustness towards adversarially generated e…
Differentiating Through Integer Linear Programs with Quadratic Regularization and Davis-Yin Splitting
Daniel McKenzie, Samy Wu Fung, Howard Heaton
In many applications, a combinatorial problem must be repeatedly solved with similar, but distinct parameters. Yet, the parameters are not directly observed; only contextual da…
Adapting Zeroth Order Algorithms for Comparison-Based Optimization
Isha Slavin, Daniel McKenzie
Comparison-Based Optimization (CBO) is an optimization paradigm that assumes only very limited access to the objective function f(x). Despite the growing relevance of CBO to real-w…