Writing on machine learning, statistics, optimisation and programming.
Recent writing
All posts-
INLA Part 1: Just keep approximating until you get the thing you want
Deriving the central results required to understand INLA, and getting us ready to implement it for some model building in later posts
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Optimisation Under Uncertainty Part 2: An LP reformulation aside
A slight diversion: how to reformulate a linear program with a nested maximisation using duality
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Optimisation Under Uncertainty Part 1: Murphy's Law
Introduction to robust optimisation under uncertainty. Part 1 covers theoretical foundations and the trade-offs between optimality and robustness.
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Building Non-Standard Regression Models with JAX
Using JAX's automatic differentiation to train a Zero-Inflated Generalised Poisson regression model
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Online Bayesian Learning Part 4: Non-stationary data and R-VGA
Adapting R-VGA to work in non-stationary environments.
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Online Bayesian Learning Part 3: R-VGA
Deriving and implementing the R-VGA algorithm for online learning with neural networks on streaming binary classification data
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Online Bayesian Learning Part 2: Bayesian Neural Networks With Variational Inference
A quick detour to introduce variational inference and use it to train a small Bayesian neural network with jax/eqx
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Online Bayesian Learning Part 1: Exact recursive inference
Part 1 of a series of posts looking into the problem of 'online' learning using Bayesian methods
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C++20: SFINAE vs. Concepts
Comparing C++20 Concepts with SFINAE for template constraints. Modern approaches to type checking and compile-time validation.