📄 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
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
📄 One paper accepted to AutoML 2025
Our paper "Ax — A Platform for Adaptive Experimentation" was accepted to AutoML 2025 (ABCD Track).
💼 Joined Meta CAS Adaptive Experimentation
Started as a Research Scientist at Meta on the Adaptive Experimentation team within Central Applied Science (CAS), based in New York City.
🎓 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 …
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
📄 One paper accepted to NeurIPS 2022
Our paper "Batch Bayesian Optimisation via Density-ratio Estimation with Guarantees", led by Rafael Oliveira, was paper accepted to NeurIPS2022!
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