I am broadly interested in the development and application of machine learning methods. Currently I focus on developing AI-based tools for data wrangling in an effort to automate the tedious tasks of data preparation and data cleaning that often precede a machine learning analysis. My past research has been on multiclass classification – typically involving SVMs – as well as meta-learning and hierarchical classifier design. I’ve also worked on regularized regression methods, where I’m mainly interested in optimization algorithms for non-convex problems. On the more practical side I have developed software packages for most of my research projects, as well as a command-line tool to automate benchmarking of machine learning methods on distributed architectures.
- Van den Burg, G.J.J. and Groenen, P.J.F. GenSVM: A Generalized Multiclass Support Vector Machine. Journal of Machine Learning Research, 17(225):1–42, 2016. [code]
- Van den Burg, G.J.J. and Nazabal, A. and Sutton, C. Wrangling Messy CSV Files by Detecting Row and Type Patterns, arXiv preprint 1811.11242, 2018. [code]
- Van den Burg, G.J.J. and Hero, A.O. Fast Meta-Learning for Adaptive Hierarchical Classifier Design, arXiv preprint 1711.03512, 2017. [code]
- Van den Burg, G.J.J. and Groenen, P.J.F. and Alfons, A. SparseStep: Approximating the Counting Norm for Sparse Regularization, arXiv Preprint 1701.06967, 2017. [code]
- Van den Burg, G.J.J. Algorithms for Multiclass Classification and Regularized Regression, Jan 2018.
- SmartSVM. Implements the SmartSVM classifier from this paper. PyPi - GitHub.
- SparseStep. Implements the SparseStep method from this paper. CRAN - GitHub.
- GenSVM. Implements the GenSVM method from this paper. PyPi - CRAN - GitHub.
- Abed. Tool for benchmarking ML methods on compute clusters. PyPi - GitHub.
- SyncRNG. The same random numbers in R and Python. CRAN - PyPi - GitHub.
- Programming – part-time lecturer, set up and pioneered the use of Autolab for this course (2015, 2016)
- Supervised two MSc thesis students in Econometrics, among whom:
- G. van Rooij, Clustering Stores of Retailers via Consumer Behavior, 2017.
- Supervised four BSc thesis students in Econometrics, among whom:
- L.W. Hoogenboom, Recommender System Optimization through Collaborative Filtering, 2016.
- E.L.J. Mathol, Neighborhood-based Collaborative Filtering: Providing the best recommendations, 2016.
- M.L. Jongsma, Categorised Neighborhood-based Collaborative Filtering, 2016.