Evaluating Synthetic Data Quality: Advanced Techniques
Chapter 1: Foundations of Synthetic Data Evaluation
Defining Data Quality Dimensions
Challenges in Evaluating Generated Data
The Fidelity-Utility-Privacy Trade-off
Taxonomy of Evaluation Metrics
Setting Up an Evaluation Environment
Chapter 2: Advanced Statistical Fidelity Assessment
Multivariate Distribution Comparisons
Hypothesis Testing for Distributional Similarity
Correlation and Covariance Structure Analysis
Information-Theoretic Measures
Propensity Score Evaluation
Hands-on practical: Implementing Multivariate Tests
Chapter 3: Evaluating Machine Learning Utility
Train-Synthetic-Test-Real (TSTR) Methodology
Train-Real-Test-Synthetic (TRTS) Methodology
Comparing Downstream Model Performance Metrics
Assessing Feature Importance Consistency
Hyperparameter Optimization Effects
Hands-on practical: Running TSTR Evaluations
Chapter 4: Privacy Assessment Techniques
Understanding Privacy Risks in Synthetic Data
Membership Inference Attacks (MIAs)
Attribute Inference Attacks
Distance-Based Privacy Metrics
Differential Privacy Considerations (if applicable)
Hands-on practical: Implementing a Basic MIA
Chapter 5: Specialized and Model-Specific Metrics
Evaluating Synthetic Images: FID, IS, Precision, Recall
Evaluating Synthetic Text: Perplexity, BLEU Scores
Evaluating Synthetic Time-Series Data
Metrics for GAN Evaluation
Metrics for VAE Evaluation
Hands-on practical: Calculating FID for Image Data
Chapter 6: Building Comprehensive Evaluation Reports
Selecting Appropriate Metrics for the Task
Automating Evaluation Pipelines
Visualizing Evaluation Results Effectively
Interpreting and Communicating Findings
Benchmarking Different Synthetic Datasets
Practice: Generating a Quality Report Snippet
Hypothesis Testing for Distributional Similarity
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- A Kernel Two-Sample Test, Arthur Gretton, Karsten M. Borgwardt, Malte J. Rasch, Bernhard Schölkopf, Alexander Smola, 2012 Journal of Machine Learning Research, Vol. 13 (Journal of Machine Learning Research) - Foundational paper introducing Maximum Mean Discrepancy (MMD) as a kernel-based two-sample test for comparing distributions.
- E-distance: A new method for testing equality of distributions, Gábor J. Székely, Maria L. Rizzo, 2007 The Annals of Applied Statistics, Vol. 1 (Institute of Mathematical Statistics) DOI: 10.1214/07-AOAS161 - Definitive paper introducing the energy distance and its use for testing equality of multivariate distributions.
- What Makes a Good Synthetic Dataset? A Review of the Metrics Used for Evaluating Synthetic Tabular Data, Sudeendra Shrikumar, Gulten Arslan, Rahul Krishnan, 2022 Transactions on Machine Learning Research (ML open-source software community) - A comprehensive review of metrics and considerations for evaluating the quality of synthetic tabular data, including MMD, energy distance, and classifier-based approaches.