Effective sampling and learning for mallows models with pairwise-preference data
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Learning preference distributions is a critical problem in many areas (e.g., recommender systems, IR, social choice). However, many existing learning and inference methods im-pose restrictive assumptions on the form of user preferences that can be admitted as ev-idence. We relax these restrictions by considering as data arbitrary pairwise comparisons of alternatives, which represent the fundamental building blocks of ordinal rankings. We develop the first algorithms for learning Mallows models (and mixtures thereof) from pair-wise comparison data. At the heart of our technique is a new algorithm, the generalized repeated insertion model (GRIM), which allows sampling from arbitrary ranking distribu-tions, and conditional Mallows models in particular. While we show that sampling from a Mallows model with pairwise evidence is computationally difficult in general, we develop approximate samplers that are exact for many important special cases—and have provable bounds with pairwise evidence—and derive algorithms for evaluating log-likelihood, learn-ing Mallows mixtures, and non-parametric estimation. Experiments on real-world data sets demonstrate the effectiveness of our approach.1
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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