Research map / Probabilistic and scientific modeling / Uncertainty and Gaussian processes
Tractable Uncertainty for Structure Learning
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 Trust-g's OrderSPN representation instead of Gadget's sample-based posterior | Gadget · Conditional query with 4 specified edges | AUROC: 0.905 ± 0.073 -> 0.903 ± 0.057 | No clear effect |
| Use Trust-g's OrderSPN representation instead of Gadget's sample-based posterior | Gadget · Conditional query with 8 specified edges | AUROC: 0.888 ± 0.089 -> 0.933 ± 0.048 | Helped |
| Use Trust-g's OrderSPN representation instead of Gadget's sample-based posterior | Gadget · Conditional query with 16 specified edges | AUROC: 0.876 ± 0.081 -> 0.957 ± 0.077 | Helped |
| Augment a seed structure-learning method with OrderSPN structure learning and variational parameter learning | Dibs and Gadget · Synthetic structure learning, d=16 and d=32 | Expected structural Hamming distance | Mixed |
| Augment a seed structure-learning method with OrderSPN structure learning and variational parameter learning | Dibs and Gadget · Synthetic structure learning, d=16 and d=32 | Marginal log-likelihood | Mixed |
| Augment a seed structure-learning method with OrderSPN structure learning and variational parameter learning | Dibs and Gadget · Synthetic structure learning, d=16 and d=32 | AUROC | Mixed |
| Augment a seed structure-learning method with OrderSPN structure learning and variational parameter learning | Dibs and Gadget · Synthetic structure learning, d=16 and d=32 | MSE of causal effects | Mixed |
| Represent the posterior with an OrderSPN rather than a sample-based approximation | Dibs and Gadget · Marginal probability of randomly selected true edges, d=16 | Non-zero query coverage | Helped |
Closest papers by meaning
- DiBS: Differentiable Bayesian Structure Learning (NeurIPS 2021 · similarity 0.90)
- ProDAG: Projected Variational Inference for Directed Acyclic Graphs (NeurIPS 2025 · similarity 0.90)
- On the Expressive Power of Tree-Structured Probabilistic Circuits (NeurIPS 2024 · similarity 0.88)
- Guided Structural Inference: Leveraging Priors with Soft Gating Mechanisms (ICML 2025 · similarity 0.88)
- Differentiable Structure Learning with Ancestral Constraints (ICML 2025 · similarity 0.86)
- Markov Equivalence and Consistency in Differentiable Structure Learning (NeurIPS 2024 · similarity 0.86)
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.