← Back to all news
arXiv Machine Learning By Aparana Gupta, Anurup Dey, Suyash Dwivedi

Beyond Marginals: A Multi-Dimensional Evaluation Framework for Multi-Table Synthetic Data Generation

Read the original on arXiv Machine Learning →

The paper introduces SynEval, a six‑dimensional framework for evaluating multi‑table synthetic data. It assesses per‑column fidelity, multivariate structure (including a new conditional distribution check), cross‑table integrity, machine‑learning utility, privacy protection, and edge‑case robustness. SynEval outputs a unified weighted quality score and is generator‑agnostic, working on any real and synthetic CSV folders with automatic schema inference.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

Related stories

arXiv Machine Learning
Oct 2

Synthesis Without Training: An Inference-Only Pipeline for Tabular, Temporal, and Relational Synthetic Data

GENSCRIPT is an inference‑only pipeline that generates synthetic data without training a generative model. It creates a deterministic statistical profile of the source data, uses a language model to infer field semantics and cross‑column constraints, and then compiles these into an executable sampler that works for single‑table, temporal, and relational data. The method builds generators in minutes, samples large datasets quickly, and achieves fidelity comparable to leading methods while preserving key data relationships such as 1‑to‑1 mappings and primary‑foreign key constraints.

By Zilong Zhao, Abdul Raheem, Jiayu Li, Sohei Arisaka, Darius Lim Hong Yi, Milad Abdollahzadeh, Uzair Javaid, Biplab Sikdar
llmsagentsbenchmarks
More like this →