Hi. My name is Louis. I am a budding machine learning researcher and PhD candidate at the University of Sydney working with Edwin Bonilla and Fabio Ramos. My main research interests lie at the intersection of Bayesian statistics, deep learning and implicit probabilistic models. This summer I am interning at Amazon Berlin, working in the Core ML team with Cédric Archambeau.
Until 2017, I was a software engineer at NICTA (now incorporated under CSIRO as Data61) in the inference systems group, working on scalable Bayesian machine learning. I now work at Data61 on a part-time basis when I am not teaching.
Previously, I studied computer science at the University of New South Wales, where I had a major emphasis on algorithm design and analysis, theoretical computer science, programming language theory, artificial intelligence, machine learning, and a minor emphasis on mathematics and statistics. I undertook my final-year thesis under Aleksandar Ignjatovic, and graduated with first-class honours in 2015.
This post demonstrates how to approximate the KL divergence (in fact, any f-divergence) between implicit distributions, using density ratio estimation by probabilistic classification.
We illustrate how to build complicated probability distributions in a modular fashion using the Bijector API from TensorFlow Probability.
An in-depth practical guide to variational encoders from a probabilistic perspective.
I am a teaching assistant (TA) for the following courses:
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. 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.