Joel Pfeiffer
Joseph J. Pfeiffer III
jpfeiffe at gmail dot com
Research
Principal ML scientist at Microsoft Bing Ads working on a variety of ML topics. I graduated with a PhD a few years ago from Purdue, studying machine learning in relational domains with Professor Jennifer Neville.
Codes
A collection of code for various models, generally written in Python for the simplest forms of representations. Several of these are done simply for my own practice/experience and I think they could be useful for someone else looking for example code. Some of these are implementations of other work and some from my own work above.
Relational Machine Learning Library
Collection of RML codes in Python!
[RMLLib]
Attributed Graph Models
Implementation of the above AGM model (WWW2014), using the simple FCL proposal distribution. Uses a simple 0/1 label value. Requires Python w/ matplotlib. The first implementation uses the Preferential Attachment model of Barabasi/Albert to create a degree distribution for AGM/FCL, while the second version learns/samples from an observed network.
[PA-AGM-FCL] [AGM-FCL]
Data Augmentation, Stochastic EM and EM
Implementation of the above ICDM2014 paper on Data Augmentation. Has some tests and comparisons between DA, Stochastic EM and EM for Naive Bayes and Logistic Regression. Implemented in C++ and contains a distribution of liblinear and eigen3. Please read and carry on any appropriate copyright notices for these works.
[Code]
Log-Linear models
A simple Python implementation for learning log-linear (maximum entropy) models. Just uses 0/1 feature/label values, and implemented for my own practice. Requires Python and scipy/numpy; I implemented both calling scipy's BFGS optimization, as well as my own GD method for fun.
[BFGS] [GD]
Publications
Please view a complete list of publications on [Google Scholar]. Some work not found on Google scholar are the privacy proposals I've been involved with -- please view the archives at [Parkeet V2] and [MaskedLARK] for predecessors to the [Private Ad Selection API].
Overcoming Uncertainty for Within-Network Relational Machine Learning
Joseph J. Pfeiffer III Ph.D. Thesis
[PDF] [BibTeX]