Research map / Probabilistic and scientific modeling / Variational and generative sampling
The Multiple Quantile Graphical Model
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use MQGM instead of SpaceJam | Conditional CDF estimation · Ring data | TV distance: 91.568 -> 20.873 | Helped |
| Use MQGM instead of TIGER | Conditional CDF estimation · Ring data | KS statistic: 1.450 -> 0.760 | Helped |
| Use MQGM instead of GLasso | Graph structure recovery · Normal, n=50 | AUC: 0.908 -> 0.953 | Helped |
| Use MQGM instead of SpaceJam | Graph structure recovery · Normal, n=100 | AUC: 0.968 -> 0.976 | Helped |
| Use MQGM instead of GLasso | Graph structure recovery · Normal, n=300 | AUC: 0.998 -> 0.988 | Hurt |
| Use MQGM instead of SpaceJam | Graph structure recovery · Student t, n=50 | AUC: 0.893 -> 0.928 | Helped |
| Use MQGM instead of SpaceJam | Graph structure recovery · Student t, n=100 | AUC: 0.965 -> 0.947 | Hurt |
| Use MQGM instead of Nonpara. | Graph structure recovery · Student t, n=300 | AUC: 0.998 -> 0.981 | Hurt |
| Use MQGM instead of SpaceJam | Graph structure recovery · Autoregressive, n=50 | AUC: 0.624 -> 0.726 | Helped |
| Use MQGM instead of SpaceJam | Graph structure recovery · Autoregressive, n=100 | AUC: 0.708 -> 0.754 | Helped |
| Use MQGM instead of SpaceJam | Graph structure recovery · Autoregressive, n=300 | AUC: 0.854 -> 0.955 | Helped |
Closest papers by meaning
- Beyond normality: Learning sparse probabilistic graphical models in the non-Gaussian setting (NeurIPS 2017 · similarity 0.88)
- Learning Local Neighborhoods of Non-Gaussian Graphical Models (AAAI 2025 · similarity 0.88)
- Fast Wasserstein rates for estimating probability distributions of probabilistic graphical models (NeurIPS 2026 · similarity 0.85)
- Scores for Learning Discrete Causal Graphs with Unobserved Confounders (AAAI 2024 · similarity 0.85)
- A Generic Family of Graphical Models: Diversity, Efficiency, and Heterogeneity (ICML 2025 · similarity 0.84)
- Causal Discovery via Transformed Low-Rank Quantile Surfaces (NeurIPS 2026 · similarity 0.84)
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