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 or fairness. In this work%2C we introduce CuTS%2C the first customizable synthetic tabular data generation framework. Customization in CuTS is achieved via declarative statistical and logical expressions%2C supporting a wide range of requirements (e.g.%2C DP or fairness%2C among others). To ensure high synthetic data quality in the presence of custom specifications%2C CuTS is pre-trained on the original dataset and fine-tuned on a differentiable loss automatically derived from the provided specifications using novel relaxations. We evaluate CuTS over four datasets and on numerous custom specifications%2C outperforming state-of-the-art specialized approaches on several tasks while being more general. In particular%2C at the same fairness level%2C we achieve 2.3%25 higher downstream accuracy than the state-of-the-art in fair synthetic data generation on the Adult dataset.?quality=80&w=800)
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