Fast maximum a posteriori inference in Monte Carlo state spaces
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Bibliographic record
Abstract
Many important algorithms for statistical inference can be expressed as a weighted maxkernel search problem.This is the case with the Viterbi algorithm for HMMs, message construction in maximum a posteriori BP (max-BP), as well as certain particlesmoothing algorithms.Previous work has focused on reducing the cost of this procedure in discrete regular grids [4].Monte-Carlo state spaces, which are vital for highdimensional inference, cannot be handled by these techniques.We present a novel dualtree based algorithm that is appliable to a wide range of kernels and shows substantial performance gains over nave computation.1 We note that there are techniques for dealing with KDE on Monte Carlo grids (fast Gauss Transform), but these are inapplicable in the max-kernel setting.2 By distance functions we mean functions that are similar to a metric but need not obey the triangle inequality.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| 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