Frangella, Zachary

8 publications

TMLR 2026 On the (linear) Convergence of Generalized Newton Inexact ADMM Zachary Frangella, Theo Diamandis, Bartolomeo Stellato, Madeleine Udell

TMLR 2025 Enhancing Physics-Informed Neural Networks Through Feature Engineering Shaghayegh Fazliani, Zachary Frangella, Madeleine Udell

NeurIPS 2025 Turbocharging Gaussian Process Inference with Approximate Sketch-and-Project Pratik Rathore, Zachary Frangella, Sachin Garg, Shaghayegh Fazliani, Michal Derezinski, Madeleine Udell

NeurIPS 2024 CRONOS: Enhancing Deep Learning with Scalable GPU Accelerated Convex Neural Networks Miria Feng, Zachary Frangella, Mert Pilanci

ICML 2024 Challenges in Training PINNs: A Loss Landscape Perspective Pratik Rathore, Weimu Lei, Zachary Frangella, Lu Lu, Madeleine Udell

JMLR 2024 PROMISE: Preconditioned Stochastic Optimization Methods by Incorporating Scalable Curvature Estimates Zachary Frangella, Pratik Rathore, Shipu Zhao, Madeleine Udell

ICML 2022 NysADMM: Faster Composite Convex Optimization via Low-Rank Approximation Shipu Zhao, Zachary Frangella, Madeleine Udell

NeurIPS 2021 Can We Globally Optimize Cross-Validation Loss? Quasiconvexity in Ridge Regression Will Stephenson, Zachary Frangella, Madeleine Udell, Tamara Broderick