Author

Kangmin Kim

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2026

Do Generative Models Keep Time? A Time-Aware Evaluation of Synthetic Sequential Tabular Data

This work presents a taxonomy-guided evaluation protocol for temporal fidelity, in which the applicable measurements are determined by the data rather than fixed in advance, and recasts utility and privacy evaluation over trajectories rather than isolated rows.

Kiwan Kwon, Kangmin Kim, Ho-Jin Lee et al. · arXiv.org · 1 citation

#machine learning Preprint Jul 2026

Seq2Synth: Benchmarking Temporal Fidelity in Synthetic Sequential Tabular Data

Seq2Synth is introduced, a unified benchmark for assessing temporal and schema properties to determine applicable evaluations, covering timestamp, cross-sectional, longitudinal, and structural fidelity, alongside trajectory-aware utility and privacy.

Kiwan Kwon, Kangmin Kim, Ho-Jin Lee et al. · 0 citations