10 papers
GPU-Enabled Large-Scale Optimization Using Randomized Linear Algebra
Pratik Rathore, Zachary Frangella, Parth Nobel +2
This paper introduces rlaopt, a PyTorch-based package for large-scale optimization and scientific computing using randomized numerical linear algebra (RandNLA). Despite substantial…
Turbocharging Gaussian Process Inference with Approximate Sketch-and-Project
Pratik Rathore, Zachary Frangella, Sachin Garg +3
Gaussian processes (GPs) play an essential role in biostatistics, scientific machine learning, and Bayesian optimization for their ability to provide probabilistic predictions and…
Enhancing Physics-Informed Neural Networks Through Feature Engineering
Shaghayegh Fazliani, Zachary Frangella, Madeleine Udell
Physics-Informed Neural Networks (PINNs) seek to solve partial differential equations (PDEs) with deep learning. Mainstream approaches that deploy fully-connected multi-layer deep…
SAPPHIRE: Preconditioned Stochastic Variance Reduction for Faster Large-Scale Statistical Learning
Jingruo Sun, Zachary Frangella, Madeleine Udell
Regularized empirical risk minimization (rERM) has become important in data-intensive fields such as genomics and advertising, with stochastic gradient methods typically used to so…
CRONOS: Enhancing Deep Learning with Scalable GPU Accelerated Convex Neural Networks
Miria Feng, Zachary Frangella, Mert Pilanci
We introduce the CRONOS algorithm for convex optimization of two-layer neural networks. CRONOS is the first algorithm capable of scaling to high-dimensional datasets such as ImageN…
Have ASkotch: A Neat Solution for Large-scale Kernel Ridge Regression
Pratik Rathore, Zachary Frangella, Jiaming Yang +2
Kernel ridge regression (KRR) is a fundamental computational tool, appearing in problems that range from computational chemistry to health analytics, with a particular interest due…