2024/02/10 by Junwei Ma, Valentin Thomas, Ma, Junwei +5 · 3 citations
Computer Science · Medicine · #Anomaly Detection Techniques and Applications #ECG Monitoring and Analysis #FOS: Computer and information sciences #Healthcare Technology and Patient Monitoring #Machine Learning (cs.LG)
paper · pdf · doi:10.48550/arxiv.2402.06971
openalex publication_date 2024/02/10 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Foundation models have revolutionized tasks in computer vision and natural language processing. However, in the realm of tabular data, tree-based models like XGBoost continue to dominate. TabPFN, a transformer model tailored for tabular data, mirrors recent foundation models in its exceptional in-context learning capability, being competitive with XGBoost's performance without the need for task-specific training or hyperparameter tuning. Despite its promise, TabPFN's applicability is hindered by its data size constraint, limiting its use in real-world scenarios. To address this, we present in-context data distillation (ICD), a novel methodology that effectively eliminates these constraints by optimizing TabPFN's context. ICD efficiently enables TabPFN to handle significantly larger datasets with a fixed memory budget, improving TabPFN's quadratic memory complexity but at the cost of a linear number of tuning steps. Notably, TabPFN, enhanced with ICD, demonstrates very strong performance against established tree-based models and modern deep learning methods on 48 large tabular datasets from OpenML.