REVIEW 4 cited by
TabPFGen -- Tabular Data Generation with TabPFN
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
Advances in deep generative modelling have not translated well to tabular data. We argue that this is caused by a mismatch in structure between popular generative models and discriminative models of tabular data. We thus devise a technique to turn TabPFN -- a highly performant transformer initially designed for in-context discriminative tabular tasks -- into an energy-based generative model, which we dub TabPFGen. This novel framework leverages the pre-trained TabPFN as part of the energy function and does not require any additional training or hyperparameter tuning, thus inheriting TabPFN's in-context learning capability. We can sample from TabPFGen analogously to other energy-based models. We demonstrate strong results on standard generative modelling tasks, including data augmentation, class-balancing, and imputation, unlocking a new frontier of tabular data generation.
Forward citations
Cited by 4 Pith papers
-
CORE: In-Context Reconstruction for Unified Tabular Anomaly Detection
CORE detects anomalies in new tabular datasets by reconstructing each test sample from the nearest normal context samples in a learned, feature-aligned space; it is proposed as the first reconstruction-based unified t...
-
Risk In Context: Benchmarking Privacy Leakage of Foundation Models in Synthetic Tabular Data Generation
LLM-based tabular generators reproduce seed rows often enough that membership-inference attacks succeed more against them than against GAN, VAE, or diffusion baselines.
-
A Closer Look on Memorization in Tabular Diffusion Model: A Data-Centric Perspective
A small subset of training samples drives most memorization in tabular diffusion models, and pruning them based on early memorization signals reduces measured leakage, though the evaluation metric makes part of the ga...
-
Privacy-Preserving Tabular Synthetic Data Generation Using TabularARGN
TabularARGN is a discretization-based auto-regressive network claimed to generate high-fidelity, privacy-robust synthetic tabular data, competitive with diffusion and GAN baselines.
Discussion (0). Sign in to comment.