Toward an Auto-Synthesizer: Stress-Aware Selection and Recommendation of Deep Tabular Generative Models

Tue, 08 Sep 2026 00:00:00 +0000

Committee: Guang Cheng (chair), Fredric Schoenberg, Michael Tsiang.

SYNTHONY: A Stress-Aware, Intent-Conditioned Agent for Deep Tabular Generative Models Selection

Tue, 31 Mar 2026 00:00:00 +0000

SYNTHONY treats synthesizer selection as a matching problem: it profiles a dataset’s “stress” — privacy sensitivity, class imbalance, cardinality, and sample size — and matches that profile against a calibrated capability registry of synthesizer families (GAN, VAE, Diffusion, DP-based, Bayesian, Normalizing Flow). It supports rule-based, LLM-based, and hybrid ranking with explainable tie-breaking and benchmark-driven comparison.

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Toward an Auto-Synthesizer: Stress-Aware Selection and Recommendation of Deep Tabular Generative Models

SYNTHONY: A Stress-Aware, Intent-Conditioned Agent for Deep Tabular Generative Models Selection