Cuong Nguyen
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Stochastic gradient and Hamiltonian Monte Carlo
This post is to introduce the formulation of stochastic gradient descent as a Monte Carlo sampling to approximate the posterior of the variables of interest.
Nov 19, 2023
Cuong Nguyen
Expectation - Maximisation algorithm and its applications for mixture models
In machine learning or statistical inference, we often encounter problems relating to
missing data
or
hidden variables
. One typical example of such latent variable models is…
Jul 17, 2022
Cuong Nguyen
Bias - variance decomposition
Bias and variance decomposition is one of the key tools to understand machine learning. However, conventional discussion about bias - variance decomposition revolves around…
May 3, 2022
Cuong Nguyen
From hyper-parameter optimisation to meta-learning
Meta-learning, also known as
learn-how-to-learning
, has been being studied from 1980s
(Schmidhuber 1987; Naik and Mammone 1992)
, and recently attracted much attention from…
Nov 22, 2021
Cuong Nguyen
Outer product approximation of Hessian matrix
Hessian matrix is heavily studied in the optimization community. The purpose is to utilize the second order derivative to optimize a function of interest (also known as…
Apr 12, 2021
Cuong Nguyen
PAC-Bayes bounds for generalisation error
Properly approaximately correct (PAC) learning is a part of
statistical machine learning
which has been a fundamental course for most of graduate programs in machine…
Dec 26, 2020
Cuong Nguyen
VAE: normalising constant matters
Variational auto-encoder (VAE) is one of the most popular generative models in machine learning nowadays. However, the rapid development of the field has made many machine…
Nov 24, 2020
Cuong Nguyen
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