198 citations · 379 across the 15 of their papers we have counts for
17 papers
Learning physics-based reduced-order models from data using nonlinear manifolds
Rudy Geelen, Laura Balzano, Stephen Wright +1
We present a novel method for learning reduced-order models of dynamical systems using nonlinear manifolds. First, we learn the manifold by identifying nonlinear structure in the d…
Cut your Losses with Squentropy
Like Hui, Mikhail Belkin, Stephen Wright
Nearly all practical neural models for classification are trained using cross-entropy loss. Yet this ubiquitous choice is supported by little theoretical or empirical evidence. Rec…
A Fully First-Order Method for Stochastic Bilevel Optimization
Jeongyeol Kwon, Dohyun Kwon, Stephen Wright +1
We consider stochastic unconstrained bilevel optimization problems when only the first-order gradient oracles are available. While numerous optimization methods have been proposed…
BOME! Bilevel Optimization Made Easy: A Simple First-Order Approach
Mao Ye, Bo Liu, Stephen Wright +2
Bilevel optimization (BO) is useful for solving a variety of important machine learning problems including but not limited to hyperparameter optimization, meta-learning, continual…
Operator inference for non-intrusive model reduction with quadratic manifolds
Rudy Geelen, Stephen Wright, Karen Willcox
This paper proposes a novel approach for learning a data-driven quadratic manifold from high-dimensional data, then employing this quadratic manifold to derive efficient physics-ba…
On the Global Convergence of Gradient Descent for multi-layer ResNets in the mean-field regime
Zhiyan Ding, Shi Chen, Qin Li +1
Finding the optimal configuration of parameters in ResNet is a nonconvex minimization problem, but first-order methods nevertheless find the global optimum in the overparameterized…