Reviewer 1 Concerns
Notation Consistency: Standardize notation throughout paper (k vs N_mfs issue)
GAN Problem Solutions: Explicitly demonstrate how method addresses mode collapse and training instability
Meta-feature Justification: Provide theoretical and empirical justification for chosen meta-features
Reviewer 2 Concerns
Advanced Meta-features: Include landmarking, model-based, and information-theoretic meta-features
Modern Baselines: Add TabDDPM, Fair-GAN variants to comparisons
Selection Analysis: Conduct systematic ablation studies on meta-feature combinations
Reviewer 3 Concerns
Theoretical Foundation: Develop mathematical framework explaining why meta-features work
High-Dimensional Performance: Test on datasets with >100 features
Automated Selection: Create data-driven meta-feature selection methods
Reviewer 4 Concerns
Conclusive Results: Design experiments that provide clearer evidence of method effectiveness
Non-Tabular Extensions: Explore applications beyond tabular data
Reviewer 1 Concerns
Notation Consistency: Standardize notation throughout paper (k vs N_mfs issue)
GAN Problem Solutions: Explicitly demonstrate how method addresses mode collapse and training instability
Meta-feature Justification: Provide theoretical and empirical justification for chosen meta-features
Reviewer 2 Concerns
Advanced Meta-features: Include landmarking, model-based, and information-theoretic meta-features
Modern Baselines: Add TabDDPM, Fair-GAN variants to comparisons
Selection Analysis: Conduct systematic ablation studies on meta-feature combinations
Reviewer 3 Concerns
Theoretical Foundation: Develop mathematical framework explaining why meta-features work
High-Dimensional Performance: Test on datasets with >100 features
Automated Selection: Create data-driven meta-feature selection methods
Reviewer 4 Concerns
Conclusive Results: Design experiments that provide clearer evidence of method effectiveness
Non-Tabular Extensions: Explore applications beyond tabular data