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How Faithful is your Synthetic Data? Sample-level Metrics for Evaluating and Auditing Generative Models., , , and . CoRR, (2021)Beyond Privacy: Navigating the Opportunities and Challenges of Synthetic Data., and . CoRR, (2023)Can You Rely on Your Model Evaluation? Improving Model Evaluation with Synthetic Test Data., , , and . CoRR, (2023)Stereotype and Skew: Quantifying Gender Bias in Pre-trained and Fine-tuned Language Models., , , , and . EACL, page 2232-2242. Association for Computational Linguistics, (2021)Practical Approaches for Fair Learning with Multitype and Multivariate Sensitive Attributes., , , and . CoRR, (2022)DECAF: Generating Fair Synthetic Data Using Causally-Aware Generative Networks., , , and . NeurIPS, page 22221-22233. (2021)Curated LLM: Synergy of LLMs and Data Curation for tabular augmentation in ultra low-data regimes., , , and . CoRR, (2023)Soft Mixture Denoising: Beyond the Expressive Bottleneck of Diffusion Models., , and . CoRR, (2023)DAUX: a Density-based Approach for Uncertainty eXplanations., , , , and . CoRR, (2022)How Faithful is your Synthetic Data? Sample-level Metrics for Evaluating and Auditing Generative Models., , , and . ICML, volume 162 of Proceedings of Machine Learning Research, page 290-306. PMLR, (2022)