Research map / Learning theory and algorithms / Combinatorial and search optimization
From Exact to Anytime Solutions for Marginal MAP
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| weight the heuristic and iteratively decrease the weight (anytime weighting) | AOBF-MMAP · Overall 175 instances, i=18 | responsiveness: 50% -> 82% | Helped |
| weight the heuristic and iteratively decrease the weight | AOBF-MMAP · Overall 175 instances, i=12 | quality score: 208% -> 365% | Helped |
| weight the heuristic with iterative weight decrease | RBFAOO-MMAP · Overall 175 instances | responsiveness: 58% -> 86% | Helped |
| weight the heuristic with iterative weight decrease | RBFAOO-MMAP · Overall 175 instances, i=12 | quality score: 90% -> 394% | Helped |
| repair the explicated search graph instead of restarting AOBF at each weight (ablation) | WAOBF · Overall 175 instances, i=12 | quality score: 365% -> 339% | Hurt |
| repair the explicated search graph instead of restarting (ablation) | WAOBF · short time bounds (t<30s) | average weight reached | Helped |
| repair instead of restart (ablation) | WAOBF · longer time bounds | average weight reached | No clear effect |
| WRBFAOO instead of BRAOBB (depth-first anytime) | BRAOBB · Overall, i=12, 1 hr | quality score: 365% -> 394% | Helped |
| WRBFAOO instead of BRAOBB | BRAOBB · promedas, i=12, 1 hr | quality score: 473% -> 305% | Hurt |
| WRBFAOO instead of exact AOBB | AOBB · grid, 1 hr | responsiveness: 93% -> 100% | Helped |
Closest papers by meaning
- Anytime Recursive Best-First Search for Bounding Marginal MAP (AAAI 2019 · similarity 0.92)
- Anytime Best+Depth-First Search for Bounding Marginal MAP (AAAI 2017 · similarity 0.92)
- Anytime Anyspace AND/OR Best-First Search for Bounding Marginal MAP (AAAI 2018 · similarity 0.92)
- Parallel AND/OR Search for Marginal MAP (AAAI 2020 · similarity 0.89)
- Look-Ahead with Mini-Bucket Heuristics for MPE (AAAI 2016 · similarity 0.89)
- Rectangle Search: An Anytime Beam Search (AAAI 2024 · similarity 0.84)
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