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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.

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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
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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 4 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
Shifting regret, mirror descent, and matrices
András György, Csaba Szepesvári
2016· article· en· Spiral (Imperial College London)· Decision Sciences
machine prediction:candidate · noneconsensus · none
9
citations
affunlabeled
On Explore-Then-Commit Strategies
Aurélien Garivier, Emilie Kaufmann, Tor Lattimore
2016· preprint· en· arXiv (Cornell University)· Decision Sciences
machine prediction:candidate · noneconsensus · none
8
citations
affunlabeled
Expressible inspections
Tai Wei Hu, Eran Shmaya
2013· article· en· Theoretical Economics· Decision Sciences
machine prediction:candidate · noneconsensus · none
8
citations
affno abstractunlabeled
Adaptive Neyman Allocation
Jinglong Zhao
2023· article· en· SSRN Electronic Journal· Decision Sciences
machine prediction:candidate · noneconsensus · none
7
citations
affunlabeled
Can We Learn to Beat the Best Stock
Allan Borodin, Ran El‐Yaniv, Vincent Gogan
2004· article· en· Journal of Artificial Intelligence Research· Decision Sciences
machine prediction:candidate · noneconsensus · none
7
citations
affunlabeled
Online Sparse Reinforcement Learning
Botao Hao, Tor Lattimore, Csaba Szepesvári, Mengdi Wang
2021· article· en· International Conference on Artificial Intelligence and Statistics· Decision Sciences
machine prediction:candidate · noneconsensus · none
6
citations
afffundno abstractunlabeled
The fast iterated bootstrap
Russell Davidson, Mirza Trokić
2020· article· en· Journal of Econometrics· Decision Sciences
machine prediction:candidate · noneconsensus · none
6
citations
affunlabeled
Combinatorial Bandits
Tor Lattimore, Csaba Szepesvári
2020· book-chapter· en· Cambridge University Press eBooks· Decision Sciences
machine prediction:candidate · noneconsensus · none
5
citations
afffundno abstractunlabeled
Bayesian Reinforcement Learning with Exploration
Tor Lattimore, Marcus Hütter
2014· book-chapter· en· Lecture notes in computer science· Decision Sciences
machine prediction:candidate · noneconsensus · none
5
citations
affunlabeled
Tracking the best quantizer
András György, Tamás Linder, Gábor Lugosi
2005· article· en· Decision Sciences
machine prediction:candidate · noneconsensus · none
5
citations
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