PACE: Plug-and-Play Contextual Embedding for Feature Screening with Pretrained Tabular Foundation Models
stat.ML, cs.LG, stat.AP, stat.ME
Submitted: 2026-09-20
Updated: 2026-09-20
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Terminology
Sources
- Scaling TabPFN: Sketching and Feature Selection for Tabular Prior-Data Fitted Networks
- ExplainerPFN: Towards tabular foundation models for model-free zero-shot feature importance estimations
- TabPFN-2.5: Advancing the State of the Art in Tabular Foundation Models
- TabPFN-3: Technical Report
- GOTabPFN: From Feature Ordering to Compact Tokenization for Tabular Foundation Models on High-Dimensional Data
- Comparing Task-Agnostic Embedding Models for Tabular Data
- TabPFN-Wide: Continued Pre-Training for Extreme Feature Counts
- SAND: One-Shot Feature Selection with Additive Noise Distortion
- Beyond IID: How General Are Tabular Foundation Models, Really?
- Label-shift robust federated feature screening for high-dimensional classification
- TabICL: A Tabular Foundation Model for In-Context Learning on Large Data
- TabICLv2: A better, faster, scalable, and open tabular foundation model
- Mitra-v2 Technical Report
- Xiaomi-TabLDM: A Tabular Foundation Model Technical Report
- Beyond Black-Box Predictions: Identifying Marginal Feature Effects in Tabular Transformer Networks
- Towards Universal Tabular Embeddings: A Benchmark Across Data Tasks
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