LLM tabular generators leak memorized numeric strings, allowing a no-box attack to achieve near-perfect membership inference on some state-of-the-art models.
Tabddpm: Modelling tabular data with diffusion models
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COMPASS formalizes HPC configuration questions as ML tasks on traces, quantifies recommendation trustworthiness, and delivers 65.93% lower average job turnaround time plus 80.93% lower node usage versus prior methods in simulator tests.
RDDG is an in-context learning system with dynamic guidance and automatic quality feedback that synthesizes high-fidelity relational data to improve imbalanced classification.
Diffusion models via DDPM work for anomaly detection but are slow; the proposed DTE method estimates diffusion time distribution analytically and with a neural net to deliver faster inference while outperforming DDPM on ADBench for unsupervised and semi-supervised settings.
Seq. RC-TGAN adds a spectral envelope loss to RC-TGAN and uses VGM discretization plus simulated benchmarks with known envelopes to generate relational time series that better match frequency-domain features.
Empirical evaluation on synthetic and real-world datasets indicates that natural experiments are present and can be leveraged via causal feature selection to boost model performance.
A temporal extension of TabDDPM generates coherent synthetic time-series sequences on the WISDM dataset that match real distributions and support downstream classification with macro F1 of 0.64.
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