Empirical Gaussian Processes
We study Empirical GPs, a principled framework for constructing flexible, data-driven Gaussian process priors. By estimating mean and covariance directly from a corpus of …
Jihao Andreas Lin
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1 min read
Ax: A Platform for Adaptive Experimentation
We present Ax, an open-source platform for adaptive experimentation built on BoTorch. Off the shelf, Ax achieves state-of-the-art performance across a wide range of synthetic and …
Miles Olson
Probabilistic Machine Learning in the Age of Deep Learning: New Perspectives for Gaussian Processes, Bayesian Optimization and Beyond (PhD Thesis)
We explore the intersection of deep learning and probabilistic machine learning, addressing limitations of Gaussian processes in comparison to neural networks and proposing …
📄 One paper accepted to ICML 2023
Our paper "Spherical Inducing Features for Orthogonally-Decoupled Gaussian Processes" was accepted to ICML2023 as an Oral Presentation!
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1 min read
Spherical Inducing Features for Orthogonally-Decoupled Gaussian Processes
We introduce spherical inter-domain inducing features that yield more flexible, data-dependent basis functions for orthogonally-decoupled GP approximations, narrowing the …
Efficient Cholesky decomposition of low-rank updates
We give a short and practical guide to efficiently computing the Cholesky decomposition of matrices perturbed by low-rank updates.
Batch Bayesian Optimisation via Density-ratio Estimation with Guarantees
We extend BORE to the batch setting and establish theoretical convergence guarantees for parallel Bayesian optimization.
rafael-oliveira
Long Talk: BORE — Bayesian Optimization by Density-Ratio Estimation
The 38th International Conference on Machine Learning (ICML 2021), virtual.
Invited Talk: BORE — Bayesian Optimization by Density-Ratio Estimation
ELLIS AutoML Seminars (virtual).
BORE: Bayesian Optimization by Density-Ratio Estimation
We reformulate the computation of the acquisition function in Bayesian optimization (BO) as a probabilistic classification problem, providing advantages in scalability, …