Synthony
Mar 31, 2026
·
1 min read
An agent that recommends the best-fit tabular generative model (GAN / VAE / Diffusion / DP-based / Bayesian / Normalizing Flow) for a given dataset by profiling its “stress” characteristics — privacy sensitivity, class imbalance, cardinality, sample size — instead of defaulting to one architecture. Implements rule-based, LLM-based, and hybrid ranking strategies with explainable tie-breaking and benchmark-driven comparison. Published at ICLR 2026 DeLTA.
Authors
Hochan Son
(he/him)
Database Architect · SRE & DevOps Engineer · ML/GenAI Practitioner
Hochan Son is a distributed systems architect and site reliability engineer
who builds data infrastructure, ML pipelines, and high-scale distributed
systems, working the gap between GenAI/LLM research and the systems that run it
in production. His graduate research at the UCLA Trustworthy AI Lab spans
synthetic tabular data generation and agentic, intent-conditioned model
selection. His background spans Adtech, entertainment, and enterprise — from
MySpace and Hallmark Labs to Branch.io and ADP.