Research map / Learning theory and algorithms / Combinatorial and search optimization
DAGs with No Fears: A Closer Look at Continuous Optimization for Learning Bayesian Networks
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Add KKTS post-processing | All tested base algorithms · ER and scale-free synthetic graphs | SHD: by at least a factor of 2 | Helped |
| Add KKTS post-processing | NOTEARS · Tested synthetic graph settings | SHD | Helped |
| Terminate NOTEARS early at a higher h tolerance before KKTS | NOTEARS-KKTS · Tested synthetic graph settings | running time: epsilon = 10^{-5} | Helped |
| Terminate NOTEARS early at a higher h tolerance before KKTS | NOTEARS-KKTS · Tested synthetic graph settings | SHD | No clear effect |
| Use Abs-KKTS instead of NOTEARS | Bayesian network structure learning · ER graphs | SHD | Helped |
| Use Abs-KKTS instead of NOTEARS | Bayesian network structure learning · ER graphs | running time | Helped |
| Add the step that removes unnecessary edge-absence constraints (ablation) | KKTS · Tested synthetic graph settings | SHD | Helped |
| Add edge reversals (ablation) | KKTS · Tested synthetic graph settings | SHD | Helped |
Closest papers by meaning
- DAGs with NO TEARS: Continuous Optimization for Structure Learning (NeurIPS 2018 · similarity 0.89)
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- Efficient Bayesian Network Structure Learning via Parameterized Local Search on Topological Orderings (AAAI 2021 · similarity 0.88)
- Bayesian Optimization with Fisher Information Geometry: Gradient Bounds and Trust-Region Methods (NeurIPS 2026 · similarity 0.86)
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