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07/14/2022
Low-Precision Arithmetic for Fast Gaussian Processes
Low-precision arithmetic has had a transformative effect on the training...
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07/13/2022
Volatility Based Kernels and Moving Average Means for Accurate Forecasting with Gaussian Processes
A broad class of stochastic volatility models are defined by systems of ...
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03/30/2022
On Uncertainty, Tempering, and Data Augmentation in Bayesian Classification
Aleatoric uncertainty captures the inherent randomness of the data, such...
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12/31/2021
When are Iterative Gaussian Processes Reliably Accurate?
While recent work on conjugate gradient methods and Lanczos decompositio...
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10/28/2021
Conditioning Sparse Variational Gaussian Processes for Online Decision-making
With a principled representation of uncertainty and closed form posterio...
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06/24/2021
Bayesian Optimization with High-Dimensional Outputs
Bayesian Optimization is a sample-efficient black-box optimization proce...
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03/02/2021
Kernel Interpolation for Scalable Online Gaussian Processes
Gaussian processes (GPs) provide a gold standard for performance in onli...
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03/02/2021
Fast Adaptation with Linearized Neural Networks
The inductive biases of trained neural networks are difficult to underst...
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02/25/2021
Loss Surface Simplexes for Mode Connecting Volumes and Fast Ensembling
With a better understanding of the loss surfaces for multilayer networks...
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03/04/2020
Rethinking Parameter Counting in Deep Models: Effective Dimensionality Revisited
Neural networks appear to have mysterious generalization properties when...
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10/29/2019
Function-Space Distributions over Kernels
Gaussian processes are flexible function approximators, with inductive b...
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07/17/2019
