Bibliographic record
Abstract
The papers in this proceedings are from the first workshop on Relational Reinforcement Learning held in conjunction with the International Conference on Machine Learning in Banff, Canada in 2004. The goal of the workshop was to foster more research in reinforcement learning as applied to domains with relational structure, and to strengthen its connections to a number of different but related fields such as inductive logic programming, speedup learning, statistical relational learning, and decision theoretic planning. The organizers would like to thank the authors and the participants for their enthusiastic response. We thank the other members of the program committee-- Andy Barto, Craig Boutilier, Saso Dzeroski, Carlos Guestrin, and Stuart Russell-- for their timely reviews in spite of the short deadlines. We would also like to acknowledge the International Machine Learning Society and the organizers and advisory committee members of the International Conference on Machine Learning for hosting the workshop. Our special thanks to Johannes Fuernkranz and Kiri Wagstaff for their valuable advice and technical support. We thank Nimish
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How this classification was reachedexpand
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 | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".