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
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 …
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
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, …
Variational Inference for Graph Convolutional Networks in the Absence of Graph Data and Adversarial Settings
We propose a joint probabilistic model with stochastic variational inference to improve the performance and robustness of graph convolutional networks (GCNs) in scenarios without …
Pantelis Elinas