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05/26/2023
An Analysis of the Johnson-Lindenstrauss Lemma with the Bivariate Gamma Distribution
Probabilistic proofs of the Johnson-Lindenstrauss lemma imply that rando...
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09/22/2022
Scalable Gaussian Process Hyperparameter Optimization via Coverage Regularization
Gaussian processes (GPs) are Bayesian non-parametric models popular in a...
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08/31/2022
Light curve completion and forecasting using fast and scalable Gaussian processes (MuyGPs)
Temporal variations of apparent magnitude, called light curves, are obse...
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05/22/2022
Fast Gaussian Process Posterior Mean Prediction via Local Cross Validation and Precomputation
Gaussian processes (GPs) are Bayesian non-parametric models useful in a ...
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07/26/2021
TriPoll: Computing Surveys of Triangles in Massive-Scale Temporal Graphs with Metadata
Understanding the higher-order interactions within network data is a key...
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07/24/2020
Scaling Graph Clustering with Distributed Sketches
The unsupervised learning of community structure, in particular the part...
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04/10/2020
Reinforcement Learning via Gaussian Processes with Neural Network Dual Kernels
While deep neural networks (DNNs) and Gaussian Processes (GPs) are both ...
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04/08/2020