8 papers
Many Optimizers But Only One Training Path: Repeated Resampling for Adaptive Optimizer Selection
Ronald Richman, Mario V. Wüthrich
An optimizer is usually chosen before training a deep neural network and then kept fixed. Treating optimizer choice as a hyperparameter could boost performance, but it requires sev…
Insurance Pricing Optimization via Off-Policy Evaluation
Sascha Günther, Dimitri Semenovich, Mario V. Wüthrich
Traditional insurance pricing relies on risk-based principles that ensure actuarial fairness and solvency but do not explicitly account for policyholders' price sensitivity. We for…
In-Context Learning Enhanced Credibility Transformer
Kishan Padayachy, Ronald Richman, Salvatore Scognamiglio +1
The starting point of our network architecture is the Credibility Transformer which extends the classical Transformer architecture by a credibility mechanism to improve model learn…
Tab-TRM: Tiny Recursive Model for Insurance Pricing on Tabular Data
Kishan Padayachy, Ronald Richman, Mario V. Wüthrich
We introduce Tab-TRM (Tabular-Tiny Recursive Model), a network architecture that adapts the recursive latent reasoning paradigm of Tiny Recursive Models (TRMs) to insurance modelin…
Reinforcement Learning for Micro-Level Claims Reserving
Benjamin Avanzi, Ronald Richman, Bernard Wong +2
Outstanding claim liabilities are revised repeatedly as claims develop, yet most modern reserving models are trained as one-shot predictors and typically learn only from settled cl…
Gini Score under Ties and Case Weights
Alexej Brauer, Mario V. Wüthrich
The Gini score is a popular tool in statistical modeling and machine learning for model validation and model selection. It is a purely rank based score that allows one to assess ri…