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4,299,418 works, Canadian by any of four routes.

Every filter state is a URL; the URL is the query; the query is citable via /q/⟨hash⟩. The page, the API and the export parse the same parameters.

The current cohort, streamed from the database: every work column, the machine labels, the provisional scores, and the per-row validation status. Exports are capped at 100,000 rows. Mints a permanent /q/ link for this exact query. The same filters always produce the same link, whoever asks.

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Advanced Bandit Algorithms Research
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Direct Codex and Gemma labels are unvalidated and sparse. Distilled predictions cover the full frame and are also unvalidated. Choose the evidence source explicitly; absence of a direct label is never a negative label.

affaffiliation
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The four routes compose: require the funder route and exclude affiliation to get the funder-only stratum no affiliation-based frame ever sees.

436 results · 1 filter active ·
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20002025
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Machine labels · sparse coverage
Evidence
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An unlabeled work is unknown, not a negative. Label coverage is reported on every query.
436 works in the cohort · of 4,299,418page 6 of 9

Labels cover 1 of 436 works in this cohort. The rest are unlabeled, which is not a negative label: the label table is sparse today and grows as labeling rounds land.

Distilled predictions cover 436 of 436 works in this cohort. Predictions are machine_predicted_unvalidated. The Gemma side is a direct model label for every work (title-only); the Codex side is a distilled, calibrated classifier. Candidate is the union; consensus is the intersection.

affunlabeled
Comparator-adaptive Convex Bandits
Dirk van der Hoeven, Ashok Cutkosky, Haipeng Luo
2020· article· en· arXiv (Cornell University)· Decision Sciences
machine prediction:candidate · noneconsensus · none
2
citations
affunlabeled
Optimal anytime regret for two experts
Nicholas J. A. Harvey, Christopher Liaw, Edwin Perkins, Sikander Randhawa
2020· article· en· Decision Sciences
machine prediction:candidate · noneconsensus · none
2
citations
affno abstractunlabeled
Markov Multi-armed Bandit
Rong Zheng, Cunqing Hua
2016· book-chapter· en· Wireless networks· Decision Sciences
machine prediction:candidate · noneconsensus · none
2
citations
fundno affunlabeled
Robust and Adaptive Planning under Model Uncertainty
Apoorva Sharma, J. Michael Harrison, Matthew Tsao, Marco Pavone
2019· preprint· en· Proceedings of the International Conference on Automated Planning and Scheduling· Decision Sciences
machine prediction:candidate · noneconsensus · none
2
citations
affno abstractunlabeled
Matroid Bayesian Online Selection
Ian DeHaan, Kanstantsin Pashkovich
2024· book-chapter· en· Lecture notes in computer science· Decision Sciences
machine prediction:candidate · noneconsensus · none
2
citations
affunlabeled
Contextual Bandits
Tor Lattimore, Csaba Szepesvári
2020· book-chapter· en· Cambridge University Press eBooks· Decision Sciences
machine prediction:candidate · noneconsensus · none
2
citations
affunlabeled
On Local Regret
Michael Bowling, Martin Zinkevich
2012· preprint· en· Decision Sciences
machine prediction:candidate · noneconsensus · none
1
citations
affunlabeled
Index
Tor Lattimore, Csaba Szepesvári
2020· paratext· en· Cambridge University Press eBooks· Decision Sciences
machine prediction:candidate · insufficient_payloadconsensus · none
1
citations
affunlabeled
Online Methods for Portfolio Selection
Tatsiana Levina
2006· book-chapter· en· IGI Global eBooks· Decision Sciences
machine prediction:candidate · noneconsensus · none
1
citations
affunlabeled
Thompson Sampling
Tor Lattimore, Csaba Szepesvári
2020· book-chapter· en· Cambridge University Press eBooks· Decision Sciences
machine prediction:candidate · noneconsensus · none
1
citations
afffundunlabeled
A Bayesian two‐armed bandit model
Xikui Wang, You Liang, Lysa Porth
2018· article· en· Applied Stochastic Models in Business and Industry· Decision Sciences
machine prediction:candidate · noneconsensus · none
1
citations
affunlabeled
No Regrets for Learning the Prior in Bandits
Soumya Basu, Branislav Kveton, Manzil Zaheer, Csaba Szepesvári
2021· preprint· en· arXiv (Cornell University)· Decision Sciences
machine prediction:candidate · noneconsensus · none
1
citations
affno abstractunlabeled
Online Learning with Variable Stage Duration
Shie Mannor, Nahum Shimkin
2006· book-chapter· en· Lecture notes in computer science· Decision Sciences
machine prediction:candidate · noneconsensus · none
1
citations
aboutno affunlabeled
An Economic Model of Induction
Nabil I. Al‐Najjar, Luciano Pomatto, Alvaro Sandroni
2013· article· en· Decision Sciences
machine prediction:candidate · noneconsensus · none
1
citations
affunlabeled
The Explore-Then-Commit Algorithm
Tor Lattimore, Csaba Szepesvári
2020· book-chapter· en· Cambridge University Press eBooks· Decision Sciences
machine prediction:candidate · noneconsensus · none
1
citations
affunlabeled
Fiduciary Bandits
Gal Bahar, Omer Ben-Porat, Kevin Leyton‐Brown, Moshe Tennenholtz
2020· article· en· International Conference on Machine Learning· Decision Sciences
machine prediction:candidate · noneconsensus · none
1
citations
affunlabeled
The Exp3-IX Algorithm
Tor Lattimore, Csaba Szepesvári
2020· book-chapter· en· Cambridge University Press eBooks· Decision Sciences
machine prediction:candidate · noneconsensus · none
1
citations
fundno affunlabeled
Strategies in the multi-armed bandit
Stanton Hudja, D. C. Woods
2025· article· en· Experimental Economics· Decision Sciences
machine prediction:candidate · noneconsensus · none
1
citations

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