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
Hyperparameter Optimization Effects
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- Hyperparameter Optimization, Matthias Feurer and Frank Hutter, 2019 (Springer, Cham) DOI: 10.1007/978-3-030-05318-5_1 - A comprehensive introduction to hyperparameter optimization methods and their use in machine learning.
- The Utility of Synthetically Generated Data: A Review, Tianqi Chen and Ruoxuan Zheng and Hui Liu and Cheng Cai, 2021 arXiv preprint arXiv:2106.01428 - Reviews methods for evaluating the utility of synthetic data for downstream machine learning tasks.
- Random Search for Hyper-Parameter Optimization, James Bergstra and Yoshua Bengio, 2012 Journal of Machine Learning Research, Vol. 13 (Microtome Publishing) DOI: 10.5555/2629706.2629721 - A paper introducing random search as an efficient and effective strategy for hyperparameter optimization.
- Synthetic Data: A Survey of Challenges and Opportunities, Jian Sun and Ning Zhang and Kui Lu and Chao Zheng and Xuelian Li, 2022 arXiv preprint arXiv:2208.06454 (arXiv) DOI: 10.48550/arXiv.2208.06454 - A survey that identifies challenges in generating high-quality synthetic data, including fidelity and utility aspects.