Ronald Richman

14 papers Journal 14

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Reinforcement Learning for Micro-Level Claims Reserving. CoRR Benjamin Avanzi, Ronald Richman, Bernard Wong, Mario V. Wüthrich, Yagebu Xie
2026	J	`jnl`	Tab-TRM: Tiny Recursive Model for Insurance Pricing on Tabular Data. CoRR Kishan Padayachy, Ronald Richman, Mario V. Wüthrich
2025	J	`jnl`	In-Context Learning Enhanced Credibility Transformer. CoRR Kishan Padayachy, Ronald Richman, Salvatore Scognamiglio, Mario V. Wüthrich
2025	J	`jnl`	Tree-like Pairwise Interaction Networks. CoRR Ronald Richman, Salvatore Scognamiglio, Mario V. Wüthrich
2024	J	`jnl`	Multiple Yield Curve Modeling and Forecasting using Deep Learning. CoRR Ronald Richman, Salvatore Scognamiglio
2024	J	`jnl`	The Credibility Transformer. CoRR Ronald Richman, Salvatore Scognamiglio, Mario V. Wüthrich
2023	J	`jnl`	Conditional expectation network for SHAP. CoRR Ronald Richman, Mario V. Wüthrich
2023	J	`jnl`	LASSO regularization within the LocalGLMnet architecture. Adv. Data Anal. Classif. Ronald Richman, Mario V. Wüthrich
2023	J	`jnl`	Smoothness and monotonicity constraints for neural networks using ICEnet. CoRR Ronald Richman, Mario V. Wüthrich
2022	J	`jnl`	A Discussion of Discrimination and Fairness in Insurance Pricing. CoRR Mathias Lindholm, Ronald Richman, Andreas Tsanakas, Mario V. Wüthrich
2022	J	`jnl`	A multi-task network approach for calculating discrimination-free insurance prices. CoRR Mathias Lindholm, Ronald Richman, Andreas Tsanakas, Mario V. Wüthrich
2022	J	`jnl`	Interpreting deep learning models with marginal attribution by conditioning on quantiles. Data Min. Knowl. Discov. Michael Merz, Ronald Richman, Andreas Tsanakas, Mario V. Wüthrich
2021	J	`jnl`	Interpreting Deep Learning Models with Marginal Attribution by Conditioning on Quantiles. CoRR Michael Merz, Ronald Richman, Andreas Tsanakas, Mario V. Wüthrich
2021	J	`jnl`	LocalGLMnet: interpretable deep learning for tabular data. CoRR Ronald Richman, Mario V. Wüthrich