1 citations · 1 across the 1 of their papers we have counts for
3 papers
cs.LG2021
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…
stat.AP2021
Embeddings and Attention in Predictive Modeling
Kevin Kuo, Ronald Richman
We explore in depth how categorical data can be processed with embeddings in the context of claim severity modeling. We develop several models that range in complexity from simple…
stat.ML2021★ 1 cited
Interpreting Deep Learning Models with Marginal Attribution by Conditioning on Quantiles
M. Merz, R. Richman, T. Tsanakas +1
A vastly growing literature on explaining deep learning models has emerged. This paper contributes to that literature by introducing a global gradient-based model-agnostic method,…