Venue: NeurIPS 2022 Workshop SyntheticData4ML

Reviews:

final_score: rating: 7: Good paper, accept
review_content: This paper presents a novel approach for generating synthetic data using vine copulas. The vine copula models are created using a hybrid of real world data and expert knowledge. As such, this paper illustrates an interesting way to incorporate prior knowledge, i.e. the structural knowledge about the variable dependencies, into the generating process. 

The synthetic data is evaluated by human experts. In future works, it would be interesting to show evaluation results in a more quantitative way.
review_title: Review

final_score: rating: 5: Marginally below acceptance threshold
review_content: The paper proposes a vine copula model to generate synthetic data for an industrial process when the available training data is limited and expert knowledge is available. 

Pros: The proposed method is simple and transparent making it easy to replicate and apply in different domains. 

Cons: The evaluation method proposed relies on assessments by experts which can be variable and highly subjective. Also the paper doesn't show any usefulness of the generated data, i.e training downstream models on the generate synthetic data and evaluating their performance. This questions the effectiveness of the proposed approach which can't merely be estimated by assessments from domain experts. 
review_title: Interesting way of generating synthetic data but unclear way to measure usefulness

Decision: Accept