Efficient tracking of large classes of experts
Bibliographic record
Abstract
In the framework of prediction with expert advice we consider prediction algorithms that compete against a class of switching strategies that can segment a given sequence into several blocks and follow the advice of a different “base” expert in each block. The performance is measured by the regret defined as the excess loss relative to the best switching strategy selected in hindsight. Our goal is to construct low-complexity prediction algorithms for the case where the set of base experts is large. In particular, starting with an arbitrary prediction algorithm A designed for the base expert class, we derive a family of efficient tracking algorithms that can be implemented with time and space complexity only O(ηγIn n) times larger than that of A, where n is the time horizon and γ ≥ 0 is a parameter of the algorithm. With A properly chosen, our algorithm achieves a regret bound of optimal order for γ >; 0, and only O(ln n) times larger than the optimal order for γ = 0 for all typical regret bound types we examined. For example, for predicting binary sequences with switching parameters, our method achieves the optimal O(ln n) regret rate with time complexity O(n1+γIn n) for any γ ϵ (0,1).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".