5 citations · 5 across the 9 of their papers we have counts for
10 papers
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…
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…
Tree-like Pairwise Interaction Networks
Ronald Richman, Salvatore Scognamiglio, Mario V. Wüthrich
Modeling feature interactions in tabular data remains a key challenge in predictive modeling, for example, as used for insurance pricing. This paper proposes the Tree-like Pairwise…
The Credibility Transformer
Ronald Richman, Salvatore Scognamiglio, Mario V. Wüthrich
Inspired by the large success of Transformers in Large Language Models, these architectures are increasingly applied to tabular data. This is achieved by embedding tabular data int…
Multiple Yield Curve Modeling and Forecasting using Deep Learning
Ronald Richman, Salvatore Scognamiglio
This manuscript introduces deep learning models that simultaneously describe the dynamics of several yield curves. We aim to learn the dependence structure among the different yiel…