Research map / Learning theory and algorithms / Submodular and game algorithms
Learning Tree Structured Potential Games
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Model observations as local optima (potential game) instead of global optima (structured prediction) (ablation) | Tree-structured potential game learner · Synthetic 6-node chain | tree structure recovery | Helped |
| Model observations as local optima instead of global optima (ablation) | Tree-structured potential game learner · Supreme Court 2013 rulings | qualitative agreement with known ideology | Helped |
| Use approximate neighborhood search with Hamming radius h=1 | Dual decomposition learner · Congressional Votes (30 senators) | recovery of party-consistent structure | Helped |
| Restrict local neighborhoods to Hamming distance h=1 instead of exhaustive enumeration | Dual decomposition learner · Complexity | per-run complexity: O(n2 * MaxIter) | Helped |
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