Louis Tiao
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PhD thesis
Probabilistic ML in the age of deep learning
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  • Blog
    • 📄 One paper accepted to ICML 2026
    • 📄 One paper accepted to AutoML 2025
    • 💼 Joined Meta CAS Adaptive Experimentation
    • 🎓 PhD thesis completed
    • PhD Thesis Acknowledgements (Unabridged)
    • 📄 One paper accepted to ICML 2023
    • Efficient Cholesky decomposition of low-rank updates
    • 📄 One paper accepted to NeurIPS 2022
    • 📄 One paper accepted to ICML 2021
    • A Primer on Pólya-gamma Random Variables - Part II: Bayesian Logistic Regression
    • An Illustrated Guide to the Knowledge Gradient Acquisition Function
    • 📄 One paper accepted to NeurIPS 2020
    • A Handbook for Sparse Variational Gaussian Processes
    • Density Ratio Estimation for KL Divergence Minimization between Implicit Distributions
    • Building Probability Distributions with the TensorFlow Probability Bijector API
    • A Tutorial on Variational Autoencoders with a Concise Keras Implementation
    • NumPy mgrid vs. meshgrid
  • Publications
    • Empirical Gaussian Processes
    • Ax: A Platform for Adaptive Experimentation
    • Probabilistic Machine Learning in the Age of Deep Learning: New Perspectives for Gaussian Processes, Bayesian Optimization and Beyond (PhD Thesis)
    • Spherical Inducing Features for Orthogonally-Decoupled Gaussian Processes
    • Batch Bayesian Optimisation via Density-ratio Estimation with Guarantees
    • BORE: Bayesian Optimization by Density-Ratio Estimation
    • Simulation-based Scoring for Model-based Asynchronous Hyperparameter and Neural Architecture Search
    • Bayesian Optimization by Density Ratio Estimation
    • Variational Inference for Graph Convolutional Networks in the Absence of Graph Data and Adversarial Settings
    • Model-based Asynchronous Hyperparameter and Neural Architecture Search
    • Variational Graph Convolutional Networks
    • Cycle-Consistent Adversarial Learning as Approximate Bayesian Inference
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  • Projects
    • Ax
    • GPflux
    • BORE
    • AutoGluon
    • Aboleth
    • revrand
  • Experience
  • Recent & Upcoming Talks
    • Long Talk: BORE — Bayesian Optimization by Density-Ratio Estimation
    • Invited Talk: BORE — Bayesian Optimization by Density-Ratio Estimation
    • Contributed Talk: BORE — Bayesian Optimization by Density-Ratio Estimation
    • Tech Talk: Cycle-Consistent Adversarial Learning as Approximate Bayesian Inference
    • Contributed Talk: Cycle-Consistent Adversarial Learning as Approximate Bayesian Inference
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