322 citations · 395 across the 13 of their papers we have counts for
4 papers · 1 filter
GAMformer: Bridging Tabular Foundation Models and Interpretable Machine Learning
Andreas Mueller, Julien Siems, Harsha Nori +4
While interpretability is crucial for machine learning applications in safety-critical domains and for regulatory compliance, existing tabular foundation models like TabPFN lack tr…
Elephants Never Forget: Memorization and Learning of Tabular Data in Large Language Models
Sebastian Bordt, Harsha Nori, Vanessa Rodrigues +2
While many have shown how Large Language Models (LLMs) can be applied to a diverse set of tasks, the critical issues of data contamination and memorization are often glossed over.…
Elephants Never Forget: Testing Language Models for Memorization of Tabular Data
Sebastian Bordt, Harsha Nori, Rich Caruana
While many have shown how Large Language Models (LLMs) can be applied to a diverse set of tasks, the critical issues of data contamination and memorization are often glossed over.…
Data Science with LLMs and Interpretable Models
Sebastian Bordt, Ben Lengerich, Harsha Nori +1
Recent years have seen important advances in the building of interpretable models, machine learning models that are designed to be easily understood by humans. In this work, we sho…