UNSUPERVISED LEARNING

GENERAL LATENT FEATURE MODELING Check out the implementation of a general Bayesian nonparametric latent feature model suitable for heterogeneous datasets. This implementation includes code for data exploration and missing data imputation.

- GENERAL LATENT FEATURE MODEL (GLFM) (Python and Matlab)

You can find more information about the GLFM in our NIPS'14 paper. Please, feel free to send any suggestions, comments, bugs or alternative implementation to melanie[at]tsc.uc3m.es or miv24[at]cam.ac.uk

CLUSTERING OF CONTINUOUS-TIME STREAMING DATA Check out the implementation of the hierarchical Dirichlet-Hawkes process (hdhp), which includes both the generation and the inference algorithm to cluster continuous-time grouped streaming data.

- HIERARCHICAL DIRICHLET-HAWKES PROCESS (HDHP) (Python)

You can find more information about the HDHP in our WWW'17 paper. Please, feel free to send any suggestions, comments, bugs or alternative implementation to cmav[at]bu.edu

SOURCE SEPARATION Check out the implementation of the infinite factorial dynamical model (iFDM), a general Bayesian non- parametric model for source separation.

- INFINITE FACTORIAL DYNAMICAL MODEL (iFDM) (Matlab)

You can find more information about the iFDM in our NIPS'15 paper. Please, feel free to send any suggestions, comments, bugs or alternative implementation to f.ruiz[at]columbia.edu or miv24[at]cam.ac.uk

COMPUTATIONAL DISCRIMINATION

Check out the implementation of fair logistic regression, which is able to provide predictions that do not discriminate with respect to one or more sensitive attributes.

- FAIR CLASSIFICATION (Python)

You can find more information about fair classifiers in our AISTATS'17 and WWW'17 papers. Please, feel free to send any suggestions, comments, bugs or alternative implementation to mzafar[at]mpi-sws.org