1.7k citations · 2.2k across the 7 of their papers we have counts for
16 papers
The Limitations of Large Width in Neural Networks: A Deep Gaussian Process Perspective
Geoff Pleiss, John P. Cunningham
Large width limits have been a recent focus of deep learning research: modulo computational practicalities, do wider networks outperform narrower ones? Answering this question has…
Rectangular Flows for Manifold Learning
Anthony L. Caterini, Gabriel Loaiza-Ganem, Geoff Pleiss +1
Normalizing flows are invertible neural networks with tractable change-of-volume terms, which allow optimization of their parameters to be efficiently performed via maximum likelih…
Hierarchical Inducing Point Gaussian Process for Inter-domain Observations
Luhuan Wu, Andrew Miller, Lauren Anderson +3
We examine the general problem of inter-domain Gaussian Processes (GPs): problems where the GP realization and the noisy observations of that realization lie on different domains.…
Bias-Free Scalable Gaussian Processes via Randomized Truncations
Andres Potapczynski, Luhuan Wu, Dan Biderman +2
Scalable Gaussian Process methods are computationally attractive, yet introduce modeling biases that require rigorous study. This paper analyzes two common techniques: early trunca…
Uses and Abuses of the Cross-Entropy Loss: Case Studies in Modern Deep Learning
Elliott Gordon-Rodriguez, Gabriel Loaiza-Ganem, Geoff Pleiss +1
Modern deep learning is primarily an experimental science, in which empirical advances occasionally come at the expense of probabilistic rigor. Here we focus on one such example; n…
Fast Matrix Square Roots with Applications to Gaussian Processes and Bayesian Optimization
Geoff Pleiss, Martin Jankowiak, David Eriksson +2
Matrix square roots and their inverses arise frequently in machine learning, e.g., when sampling from high-dimensional Gaussians or whitening a…