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Large-Scale Gaussian Processes via Alternating Projection
Kaiwen Wu, Jonathan Wenger, Haydn Jones +2
Training and inference in Gaussian processes (GPs) require solving linear systems with kernel matrices. To address the prohibitive time complexity, r…
CoLA: Exploiting Compositional Structure for Automatic and Efficient Numerical Linear Algebra
Andres Potapczynski, Marc Finzi, Geoff Pleiss +1
Many areas of machine learning and science involve large linear algebra problems, such as eigendecompositions, solving linear systems, computing matrix exponentials, and trace esti…
Sharp Calibrated Gaussian Processes
Alexandre Capone, Geoff Pleiss, Sandra Hirche
While Gaussian processes are a mainstay for various engineering and scientific applications, the uncertainty estimates don't satisfy frequentist guarantees and can be miscalibrated…
Pathologies of Predictive Diversity in Deep Ensembles
Taiga Abe, E. Kelly Buchanan, Geoff Pleiss +1
Classic results establish that encouraging predictive diversity improves performance in ensembles of low-capacity models, e.g. through bagging or boosting. Here we demonstrate that…