Research map / Probabilistic and scientific modeling / Uncertainty and Gaussian processes
T-LoHo: A Bayesian Regularization Model for Structured Sparsity and Smoothness on Graphs
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use T-LoHo instead of the strongest listed baseline (FL) | Graph-structured regression · 30×30 lattice simulation (ϑ=0, SNR=2) | MSPE: 85.0(20.0) -> 68.5(30.0) | Helped |
| Use T-LoHo instead of the strongest listed baseline (FL) | Graph-structured regression · 30×30 lattice simulation (ϑ=0, SNR=4) | MSPE: 55.8(14.2) -> 24.4(19.6) | Helped |
| Use T-LoHo instead of the strongest listed baseline (STGP) | Graph-structured regression · 30×30 lattice simulation (ϑ=3, SNR=2) | MSPE: 278.0(53.0) -> 251.0(112.0) | Helped |
| Use T-LoHo instead of the strongest listed baseline (FL) | Graph-structured regression · 30×30 lattice simulation (ϑ=3, SNR=4) | MSPE: 115.8(36.1) -> 59.7(23.2) | Helped |
| Use T-LoHo instead of the strongest listed baseline (STGP) | Graph-structured regression · 30×30 lattice simulation (ϑ=0, SNR=2) | Rand index: 0.72(0.09) -> 0.88(0.06) | Helped |
| Use T-LoHo instead of the strongest listed baseline (STGP) | Graph-structured regression · 30×30 lattice simulation (ϑ=0, SNR=4) | Rand index: 0.72(0.10) -> 0.95(0.05) | Helped |
| Use T-LoHo instead of the strongest listed baseline (STGP) | Graph-structured regression · 30×30 lattice simulation (ϑ=3, SNR=2) | Rand index: 0.79(0.04) -> 0.87(0.04) | Helped |
| Use T-LoHo instead of the strongest listed baseline (STGP) | Graph-structured regression · 30×30 lattice simulation (ϑ=3, SNR=4) | Rand index: 0.80(0.03) -> 0.95(0.02) | Helped |
| Use a horseshoe prior rather than a normal prior for cluster means | T-LoHo cluster-merge decisions · Two-cluster comparison | Bayes factor favoring one merged cluster | Mixed |
| Use T-LoHo rather than fused lasso | Manhattan road-network anomaly detection · 2011 NYC Pride March | Estimation bias | Helped |
Closest papers by meaning
- Learning Local Neighborhoods of Non-Gaussian Graphical Models (AAAI 2025 · similarity 0.87)
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- Beyond normality: Learning sparse probabilistic graphical models in the non-Gaussian setting (NeurIPS 2017 · similarity 0.87)
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