Research map / Learning theory and algorithms / Sample complexity
Distributionally Robust Skeleton Learning of Discrete Bayesian Networks
Read the paperCheck your idea against 108,967 papers
AI-extracted summary. Written by AI from the paper's title only (no summary yet). Numbers and claims can be wrong or missing context, so check the paper before you cite or build on it.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use Wasserstein DRO instead of MMPC | Discrete Bayesian network skeleton learning · earthquake, Huber, ζ=0.2 | F1-score: 0.5978 -> 0.7509 | Helped |
| Use Wasserstein DRO instead of MMPC | Discrete Bayesian network skeleton learning · earthquake, independent failure, ζ=0.2 | F1-score: 0.0000 -> 0.6786 | Helped |
| Use Wasserstein DRO instead of MMPC | Discrete Bayesian network skeleton learning · cancer, Huber, ζ=0.5 | F1-score: 0.6133 -> 0.9156 | Helped |
| Use Wasserstein DRO instead of MMPC | Discrete Bayesian network skeleton learning · asia, independent failure, ζ=0.2 | F1-score: 0.6359 -> 0.6933 | Helped |
| Use Wasserstein DRO instead of group regularization | Discrete Bayesian network skeleton learning · alarm, Huber, ζ=0.2 | F1-score: 0.6571 -> 0.1432 | Hurt |
| Use Wasserstein DRO instead of group regularization | Discrete Bayesian network skeleton learning · alarm, independent failure, ζ=0.2 | F1-score: 0.5458 -> 0.1419 | Hurt |
| Use Wasserstein DRO instead of MMPC | Discrete Bayesian network skeleton learning · asia, noisefree, ζ=0 | F1-score: 0.9067 -> 0.7800 | Hurt |
| Use Wasserstein DRO instead of MMPC | Discrete Bayesian network skeleton learning · cancer, noisefree, ζ=0 | F1-score: 0.6133 -> 1.0000 | Helped |
| Use Wasserstein DRO instead of MMPC | Discrete Bayesian network skeleton learning · earthquake, noisefree, ζ=0 | F1-score: 1.0000 -> 0.8447 | Hurt |
Closest papers by meaning
- Robust Learning of Fixed-Structure Bayesian Networks (NeurIPS 2018 · similarity 0.91)
- Bayesian Nonparametrics Meets Data-Driven Distributionally Robust Optimization (NeurIPS 2024 · similarity 0.86)
- Distribution Learning Meets Graph Structure Sampling (NeurIPS 2025 · similarity 0.86)
- Wasserstein Distributionally Robust Optimization through the Lens of Structural Causal Models and Individual Fairness (NeurIPS 2024 · similarity 0.85)
- Achievable distributional robustness when the robust risk is only partially identified (NeurIPS 2024 · similarity 0.85)
- Structure-informed Risk Minimization for Robust Ensemble Learning (ICML 2025 · similarity 0.85)
Writing a paper in this area? scime draft is a free LaTeX editor with AI included, real-time co-authors and a check of your references and venue rules before you submit. It also imports OpenAI Prism projects.