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01/30/2022
Scaling Gaussian Process Optimization by Evaluating a Few Unique Candidates Multiple Times
Computing a Gaussian process (GP) posterior has a computational cost cub...
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01/17/2022
Efficient Hyperparameter Tuning for Large Scale Kernel Ridge Regression
Kernel methods provide a principled approach to nonparametric learning. ...
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10/21/2021
Mean Nyström Embeddings for Adaptive Compressive Learning
Compressive learning is an approach to efficient large scale learning ba...
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06/23/2021
ParK: Sound and Efficient Kernel Ridge Regression by Feature Space Partitions
We introduce ParK, a new large-scale solver for kernel ridge regression....
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06/16/2021
Ada-BKB: Scalable Gaussian Process Optimization on Continuous Domain by Adaptive Discretization
Gaussian process optimization is a successful class of algorithms (e.g. ...
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06/18/2020
Kernel methods through the roof: handling billions of points efficiently
Kernel methods provide an elegant and principled approach to nonparametr...
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06/10/2020
On Mixup Regularization
Mixup is a data augmentation technique that creates new examples as conv...
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02/23/2020
Near-linear Time Gaussian Process Optimization with Adaptive Batching and Resparsification
Gaussian processes (GP) are one of the most successful frameworks to mod...
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03/13/2019
Gaussian Process Optimization with Adaptive Sketching: Scalable and No Regret
Gaussian processes (GP) are a popular Bayesian approach for the optimiza...
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10/31/2018
On Fast Leverage Score Sampling and Optimal Learning
Leverage score sampling provides an appealing way to perform approximate...
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07/17/2018
Learning with SGD and Random Features
Sketching and stochastic gradient methods are arguably the most common t...
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05/31/2017