Louis Tiao is a machine learning researcher and PhD candidate at the University of Sydney. His research interests are in probabilistic machine learning, and specifically includes approximate Bayesian inference, Gaussian processes, and its applications to areas such as Bayesian optimization and graph representation learning.
Ph.D. in Computer Science (Machine Learning), 2022 (expected)
University of Sydney
B.Sc. (Honours 1st Class) in Computer Science (Artificial Intelligence and Mathematics), 2015
University of New South Wales
After NICTA was subsumed under CSIRO (Australia’s national science agency), I continued as a member of the Inference Systems Engineering team, working to apply probabilistic machine learning to a multitude of problem domains, including spatial inference and Bayesian experimental design, with an emphasis on scalability. In this time, I led the design and implementation of new microservices and contributed to the development of open-source libraries for large-scale Bayesian deep learning.
During this period, I also served a brief stint with the Graph Analytics Engineering team (the team behind StellarGraph), where I contributed to research into graph representation learning from a probabilistic perspective. These efforts culminated in a research paper that went on to be awarded a spotlight presentation at the field’s premier conference.
We reformulate the computation of the acquisition function in Bayesian optimization (BO) as a probabilistic classification problem, providing advantages in scalability, flexibility, and representational capacity, while casting aside the limitations of tractability constraints on the model.
Our proposed framework uses a joint probabilistic model and stochastic variational inference to improve the performance and robustness of graph convolutional networks (GCNs) in scenarios without input graph data, outperforming state-of-the-art algorithms on semi-supervised classification tasks.
We introduce a model-based method for asynchronous multi-fidelity hyperparameter and neural architecture search that combines the strengths of asynchronous Hyperband and Gaussian process-based Bayesian optimization, achieving substantial speed-ups over current state-of-the-art methods on challenging benchmarks for tabular data, image classification, and language modeling.
One weird trick to make exact inference in Bayesian logistic regression tractable.
This series explores market data provided by official API from Binance, one of the world’s largest cryptocurrency exchanges, using Python. In this post we examine various useful ways to visualize the recent and historical trades.
A short illustrated reference guide to the Knowledge Gradient acquisition function with an implementation from scratch in TensorFlow Probability.
This series explores market data provided by official API from Binance, one of the world’s largest cryptocurrency exchanges, using Python. In this post we examine various useful ways to visualize the orderbook.
A summary of notation, identities and derivations for the sparse variational Gaussian process (SVGP) framework.
The course has a primary focus on probabilistic machine learning methods, covering the topics of exact and approximate inference in directed and undirected probabilistic graphical models - continuous latent variable models, structured prediction models, and non-parametric models based on Gaussian processes.
This course has a major emphasis on maintaining a good balance between theory and practice. As the teaching assistant (TA) for this course, my primary responsibility was to create lab exercises that aid students in gaining hands-on experience with these methods, specifically applying them to real-world data using the most current tools and libraries. The labs were Python-based, and relied heavily on the Python scientific computing and data analysis stack (NumPy, SciPy, Matplotlib, Seaborn, Pandas, IPython/Jupyter notebooks), and the popular machine learning libraries scikit-learn and TensorFlow.
Students were given the chance to experiment with a broad range of methods on various problems, such as Markov chain Monte Carlo (MCMC) for Bayesian logistic regression, probabilistic PCA (PPCA), factor analysis (FA) and independent component analysis (ICA) for dimensionality reduction, hidden Markov models (HMMs) for speech recognition, conditional random fields (CRFs) for named-entity recognition, and Gaussian processes (GPs) for regression and classification.