📄 One paper accepted to ICML 2026
Our paper "Empirical Gaussian Processes" was accepted to ICML 2026.
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
🎓 PhD thesis completed
Submitted my PhD thesis, *Probabilistic Machine Learning in the Age of Deep Learning*, at the University of Sydney.
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 …
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).