7 citations · 12 across the 7 of their papers we have counts for
5 papers · 1 filter
Conditional expectation network for SHAP
Ronald Richman, Mario V. Wüthrich
A very popular model-agnostic technique for explaining predictive models is the SHapley Additive exPlanation (SHAP). The two most popular versions of SHAP are a conditional expecta…
Smoothness and monotonicity constraints for neural networks using ICEnet
Ronald Richman, Mario Wüthrich
Deep neural networks have become an important tool for use in actuarial tasks, due to the significant gains in accuracy provided by these techniques compared to traditional methods…
A Discussion of Discrimination and Fairness in Insurance Pricing
Mathias Lindholm, Ronald Richman, Andreas Tsanakas +1
Indirect discrimination is an issue of major concern in algorithmic models. This is particularly the case in insurance pricing where protected policyholder characteristics are not…
A multi-task network approach for calculating discrimination-free insurance prices
Mathias Lindholm, Ronald Richman, Andreas Tsanakas +1
In applications of predictive modeling, such as insurance pricing, indirect or proxy discrimination is an issue of major concern. Namely, there exists the possibility that protecte…
LocalGLMnet: interpretable deep learning for tabular data
Ronald Richman, Mario V. Wüthrich
Deep learning models have gained great popularity in statistical modeling because they lead to very competitive regression models, often outperforming classical statistical models…