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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
fundfunder
venuejournal
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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 9 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.

fundno affunlabeled
Stochastic Gradient Succeeds for Bandits
Jincheng Mei, Zixin Zhong, Bo Dai, Alekh Agarwal, Csaba Szepesvári, Dale Schuurmans
2024· preprint· en· arXiv (Cornell University)· Decision Sciences
machine prediction:candidate · noneconsensus · none
0
citations
affunlabeled
On Data-driven Multi-Product Pricing
Tianyu Wang, Chenye Wu, Wei Qi
2021· article· en· Decision Sciences
machine prediction:candidate · noneconsensus · none
0
citations
affunlabeled
Asymptotically Optimal Information-Directed Sampling
Johannes Kirschner, Tor Lattimore, Claire Vernade, Csaba Szepesvári
2021· article· en· Conference on Learning Theory· Decision Sciences
machine prediction:candidate · noneconsensus · none
0
citations
affunlabeled
Fiduciary Bandits
Gal Bahar, Omer Ben-Porat, Kevin Leyton‐Brown, Moshe Tennenholtz
2019· preprint· en· arXiv (Cornell University)· Decision Sciences
machine prediction:candidate · noneconsensus · none
0
citations
affunlabeled
An Online Learning Theory of Brokerage
Nataša Bolić, Tommaso Cesari, Roberto Colomboni
2024· article· Decision Sciences
machine prediction:candidate · noneconsensus · none
0
citations
afffundunlabeled
Best-case lower bounds in online learning
Cristóbal Guzmán, Nishant A. Mehta, Ali Mortazavi
2021· article· en· University of Twente Research Information· Decision Sciences
machine prediction:candidate · noneconsensus · none
0
citations
venueno affunlabeled
Estimating Smooth and Convex Functions
Eunji Lim, Kihwan Kim
2020· article· en· International Journal of Statistics and Probability· Decision Sciences
machine prediction:candidate · noneconsensus · none
0
citations
affno abstractunlabeled
On the bandit problem
Ulrich Herkenrath, Radu Theodurescu
2005· book-chapter· en· Lecture notes in control and information sciences· Decision Sciences
machine prediction:candidate · noneconsensus · none
0
citations
affunlabeled
An Empirical Study of Neural Kernel Bandits
Michal Lisicki, Arash Afkanpour, Graham W. Taylor
2021· preprint· en· arXiv (Cornell University)· Decision Sciences
machine prediction:candidate · noneconsensus · none
0
citations
affunlabeled
Preface
Tor Lattimore, Csaba Szepesvári
2020· other· en· Decision Sciences
machine prediction:candidate · noneconsensus · none
0
citations

How this was built: Screen · Findings · About