Estimating Uncoded Crash Factors with Tabular Foundation and System One Models: Kumo Tabular and Jev
stat.AP, cs.LG, stat.ME
Submitted: 2026-10-07
Updated: 2026-10-07
Comments: 26 pages, 9 figures, 8 tables. Code: https://github.com/pozapas/kumo-jev-crash-records
Code: https://github.com/pozapas/kumo-jev-crash-records
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Sources
- PPI++: Efficient Prediction-Powered Inference
- Accuracy is Not Agreement: Expert-Aligned Evaluation of Crash Narrative Classification Models
- Benchmarking Frontier Large Language Models Against Official Crash Database Coding Using Police Crash Narratives
- Design-Based Supervised Learning with Noisy Human Labels
- Using Imperfect Surrogates for Downstream Inference: Design-based Supervised Learning for Social Science Applications of Large Language Models
- TabArena: A Living Benchmark for Machine Learning on Tabular Data
- TabPFN-3: Technical Report
- Noise Immunity in In-Context Tabular Learning: An Empirical Robustness Analysis of TabPFN's Attention Mechanisms
- Evaluating Decision Models for Text Annotation in Computational Social Science
- TabPFN-3.5: Technical Report
- M-estimation under Two-Phase Multiwave Sampling with Applications to Prediction-Powered Inference
- Feature Group Tabular Transformer: A Novel Approach to Traffic Crash Modeling and Causality Analysis
- Predicting person-level injury severity using crash narratives: A balanced approach with roadway classification and natural language process techniques
- CatBoost: unbiased boosting with categorical features
- Beyond IID: How General Are Tabular Foundation Models, Really?
- TabICLv2: A better, faster, scalable, and open tabular foundation model
- Calibrated Decisions at Scale: Converting Police Crash Narratives into Probabilistic Crash Variables with a System One Model (Jev)
- Tabular Data with Class Imbalance: Predicting Electric Vehicle Crash Severity with Pretrained Transformers (TabPFN) and Mamba-Based Models
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