5 citations · 5 across the 9 of their papers we have counts for
6 papers · 1 filter
Scaling Laws, Tabular Data and Actuarial Ratemaking Models
Ronald Richman
Scaling laws in modern deep learning describe how held-out loss improves as model capacity, training data, and compute increase, often following power-law trends. We investigate wh…
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…
A Note on the Generalized Cape Cod Reserving Method
Ronald Richman, Mario V. Wüthrich
Claims reserving is one of the most important actuarial tasks in non-life insurance modeling. There are several popular methods to perform claims reserving such as the chain-ladder…
One-Shot Individual Claims Reserving
Ronald Richman, Mario V. Wüthrich
Individual claims reserving has not yet become established in actuarial practice. We attribute this to the absence of a satisfactory methodology: existing approaches tend to be eit…
From Chain-Ladder to Individual Claims Reserving
Ronald Richman, Mario V. Wüthrich
The chain-ladder (CL) method is the most widely used claims reserving technique in non-life insurance. This manuscript introduces a novel approach to computing the CL reserves base…
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…