Interpretable Two-Sample Test – The goal of this project is to learn a set of features to distinguish two given distributions P and Q, as observed through two samples. This task is formulated as a two-sample test problem. (05/2016)
Locally Linear Latent Variable Model (LL-LVM) – LL-LVM is a probabilistic model for non-linear manifold discovery that describes a joint distribution over observations, their manifold coordinates and locally linear maps conditioned on a set of neighbourhood relationships. (09/2015)
Learning to Pass EP Messages – In this project, we propose learning a kernel-based message operator that replaces the multivariate integral required in classical EP to compute an outgoing message given incoming messages. The operator allows fast computations of outgoing messages and can be updated online cheaply during EP inference. (03/2015)
$\ell_1$-LSMI – A supervised feature selection algorithm based on a squared-loss variant of mutual information. Implementation is available in Matlab. (03/2013)
Classifier-based Thai Word Tokenizer – Decision tree-based Java library to tokenize Thai text. The project was finished in two months for a competition. Warning: Not for production use. Detail is in this presentation file. (02/2010)
JTCC – Rule-based Java library to tokenize Thai text into a list of Thai Character Clusters (TCC). See its github page for details. (03/2010)