One Pipeline, Many Transformers: Pattern-Specific Imputation Specialists for Tabular Missing Data

arXiv:2510.02625 · cs.LG · Submitted 2025-10-03 · Read on arXiv

cs.LG

Submitted: 2025-10-03

Updated: 2026-08-28

Code: https://github.com/PriorLabs/tabpfn-extensions

Terminology

• Transformer — 13× in this paper · explained in 2 episodes
• Benchmark — 11× in this paper · explained in 2 episodes
• Matrix Completion — 9× in this paper · explained in 4 episodes
• Foundation Model — 8× in this paper · explained in 3 episodes
• Missing Not At Random — 6× in this paper
• Accuracy — 4× in this paper · explained in 1 episode
• Foundation Models — 4× in this paper · explained in 7 episodes
• Optimal Transport — 4× in this paper · explained in 5 episodes
• Representation Learning — 4× in this paper · explained in 2 episodes
• In-Context Learning — 3× in this paper · explained in 4 episodes
• Selection Bias — 3× in this paper · explained in 4 episodes
• Attention Mechanism — 2× in this paper · explained in 3 episodes
• Bayesian Inference — 2× in this paper · explained in 5 episodes
• Core — 2× in this paper · explained in 2 episodes
• Missing Completely At Random — 2× in this paper
• Adam — 1× in this paper · explained in 2 episodes
• AdamW Optimizer — 1× in this paper · explained in 1 episode
• Bayesian Framework — 1× in this paper · explained in 3 episodes
• Catastrophic Forgetting — 1× in this paper · explained in 5 episodes
• Clustering — 1× in this paper · explained in 2 episodes
• Consistency — 1× in this paper · explained in 3 episodes
• Cooperation — 1× in this paper · explained in 2 episodes
• Diffusion Model — 1× in this paper · explained in 8 episodes
• Generalizability — 1× in this paper · explained in 6 episodes
• In-Context Learning (ICL) — 1× in this paper · explained in 5 episodes
• Latent Space — 1× in this paper · explained in 7 episodes
• Loss Function — 1× in this paper · explained in 2 episodes
• Meta-learning — 1× in this paper · explained in 2 episodes
• Missing At Random — 1× in this paper
• Regularization — 1× in this paper · explained in 3 episodes
• Synthetic Data Generation — 1× in this paper · explained in 5 episodes
• Transformer Architecture — 1× in this paper · explained in 2 episodes
• alpha — 1× in this paper · explained in 1 episode

Sources

• N squared: A Unified Python Package and Test Bench for Nearest Neighbor-Based Matrix Completion
• ReMasker: Imputing Tabular Data with Masked Autoencoding
• CACTI: Leveraging Copy Masking and Contextual Information to Improve Tabular Data Imputation
• not-MIWAE: Deep Generative Modelling with Missing not at Random Data
• Decoupled Weight Decay Regularization
• TabDPT: Scaling Tabular Foundation Models on Real Data
• TabICL: A Tabular Foundation Model for In-Context Learning on Large Data
• A Closer Look at TabPFN v2: Understanding Its Strengths and Extending Its Capabilities
• TabFlex: Scaling Tabular Learning to Millions with Linear Attention
• DiffPuter: Empowering Diffusion Models for Missing Data Imputation

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