5 citations · 5 across the 9 of their papers we have counts for
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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…
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…
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…