Featured Publications
Sparse Regression with Multi-type Regularized Feature Modeling
We develop the SMuRF algorithm that accurately and effectively solves the general problem of convex optimization with a differentiable loss function and multi-type penalty terms. We show in full detail how this algorithm creates sparse models, using varying combinations of Lasso-type penalties, and investigating and documenting all possible model choices.
Boosting insights in insurance tariff plans with tree-based machine learning methods
We adapt tree-based machine learning methods to the problem of insurance pricing, thereby leaving the comfort zone of both traditional ratemaking and machine learning. State-ofthe-art GLMs are compared to regression trees, random forests and gradient boosting machines.
