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
Information-Theoretic Measures
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- Elements of Information Theory, Thomas M. Cover and Joy A. Thomas, 2006 (John Wiley & Sons) DOI: 10.1002/047174882X - A standard textbook that provides a foundation in information theory, detailing concepts like entropy, mutual information, and divergence measures.
- sklearn.metrics.mutual_info_score, scikit-learn developers, 2023 - Official documentation for calculating mutual information between discrete labels or a contingency matrix, useful for comparing dependencies in data.
- scipy.stats.entropy, SciPy developers, 2023 - Official documentation describing the computation of Shannon entropy and Kullback-Leibler divergence for probability distributions.
- Quantifying the statistical quality of synthetic data generated by deep learning models, Lingjiao Zhang, Jiaqi Gao, Xiaoyong Liang, Jiawei Zhang, Yihao Feng, Kai Li, Jia Liu, Sanjeev R. Kulkarni, 2020 Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 34 (AAAI Press) DOI: 10.1609/aaai.v34i06.6727 - A research paper that examines various metrics, including information-theoretic measures, for assessing the statistical quality of synthetic data.