Relational logistic regression: the directed analog of Markov logic networks
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Bibliographic record
Abstract
www-ai.cs.uni-dortmund.de/PERSONAL/kersting.html homes.soic.indiana.edu/natarasr / cs.ubc.ca/˜poole/ Relational logistic regression (RLR) was presented at the 14th International Conference on Principles of Knowledge Representation and Reasoning (KR-2014). RLR is the di-rected analogue of Markov logic networks. Whereas Markov logic networks define distributions in terms of weighted for-mulae, RLR defines conditional probabilities in terms of weighted formulae. They agree for the supervised learning case when all variables except a query leaf variable are ob-served. However, they are quite different in representing dis-tributions. The KR-2014 paper defined the RLR formalism, defined canonical forms for RLR in terms of positive con-junctive (or disjunctive) formulae, indicated the class of con-ditional probability distributions that can and cannot be repre-sented by RLR, and defined many other aggregators in terms of RLR. In this paper, we summarize these results and com-pare RLR to Markov logic networks.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it