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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
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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 7 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
Stochastic Bandits with Finitely Many Arms
Tor Lattimore, Csaba Szepesvári
2020· book-chapter· en· Cambridge University Press eBooks· Decision Sciences
machine prediction:candidate · noneconsensus · none
1
citations
affunlabeled
Optimal anytime regret with two experts
Nicholas J. A. Harvey, Christopher Liaw, Edwin Perkins, Sikander Randhawa
2023· article· en· Mathematical Statistics and Learning· Decision Sciences
machine prediction:candidate · noneconsensus · none
1
citations
affunlabeled
Regime Switching Bandits
Xiang Zhou, Yi Xiong, Ningyuan Chen, Xuefeng Gao
2021· article· en· Neural Information Processing Systems· Decision Sciences
machine prediction:candidate · noneconsensus · none
1
citations
affno abstractunlabeled
Optimal Exploration
David Austen‐Smith, César Martinelli
2018· article· en· SSRN Electronic Journal· Decision Sciences
machine prediction:candidate · noneconsensus · none
1
citations
affunlabeled
Introduction
Tor Lattimore, Csaba Szepesvári
2020· book-chapter· en· Cambridge University Press eBooks· Decision Sciences
machine prediction:candidate · insufficient_payloadconsensus · insufficient_payload
1
citations
afffundunlabeled
Scalable Delay-Sensitive Polling of Sensors
Hootan Rashtian, Bader Alahmad, Sathish Gopalakrishnan
2020· article· en· IEEE Access· Decision Sciences
machine prediction:candidate · noneconsensus · none
1
citations
affunlabeled
Safe Linear Thompson Sampling With Side Information
Ahmadreza Moradipari, Sanae Amani, Mahnoosh Alizadeh, Christos Thrampoulidis
2021· preprint· en· IEEE Transactions on Signal Processing· Decision Sciences
machine prediction:candidate · noneconsensus · none
1
citations
affunlabeled
Toppling conjectures
Alex Fink, Richard J. Nowakowski, Aaron Siegel, David A. Wolfe
2015· other· en· Decision Sciences
machine prediction:candidate · noneconsensus · none
0
citations
affno abstractunlabeled
Online Learning for Dynamic Service Mode Control
Wenqian Xing, Yue Hu, Anand Kalvit, Vahid Sarhangian
2025· preprint· en· SSRN Electronic Journal· Decision Sciences
machine prediction:candidate · noneconsensus · none
0
citations
affunlabeled
Meta-Descent for Online, Continual Prediction
Andrew Jacobsen, Matthew Schlegel, Cameron Linke, Thomas Degris, Adam White, Martha White
2019· preprint· en· Proceedings of the AAAI Conference on Artificial Intelligence· Decision Sciences
machine prediction:candidate · noneconsensus · none
0
citations
affunlabeled
On Data-Driven Multi-Product Pricing
Tianyu Wang, Chenye Wu, Wei Qi
2020· article· en· IEEE Control Systems Letters· Decision Sciences
machine prediction:candidate · noneconsensus · none
0
citations
affunlabeled
Multi-Environment Meta-Learning in Stochastic Linear Bandits
Ahmadreza Moradipari, Mohammad Ghavamzadeh, Taha Rajabzadeh, Christos Thrampoulidis, Mahnoosh Alizadeh
2022· article· en· 2022 IEEE International Symposium on Information Theory (ISIT)· Decision Sciences
machine prediction:candidate · noneconsensus · none
0
citations
affunlabeled
Ranking
Tor Lattimore, Csaba Szepesvári
2020· book-chapter· en· Cambridge University Press eBooks· Decision Sciences
machine prediction:candidate · noneconsensus · none
0
citations
affunlabeled
Partial Monitoring
Tor Lattimore, Csaba Szepesvári
2020· book-chapter· en· Cambridge University Press eBooks· Decision Sciences
machine prediction:candidate · noneconsensus · none
0
citations
afffundunlabeled
X-Armed Bandits
Sébastien Bubeck, Rémi Munos, Gilles Stoltz, Csaba Szepesvári
2010· preprint· en· arXiv (Cornell University)· Decision Sciences
machine prediction:candidate · noneconsensus · none
0
citations
affunlabeled
Beyond Bandits
Tor Lattimore, Csaba Szepesvári
2020· book-chapter· en· Cambridge University Press eBooks· Decision Sciences
machine prediction:candidate · noneconsensus · none
0
citations
affunlabeled
Stochastic Linear Bandits with Sparsity
Tor Lattimore, Csaba Szepesvári
2020· book-chapter· en· Cambridge University Press eBooks· Decision Sciences
machine prediction:candidate · noneconsensus · none
0
citations
venueno affno abstractunlabeled
10.1145/3768292.3793394
2000· article· en· Time to knit· Decision Sciences
machine prediction:candidate · insufficient_payloadconsensus · insufficient_payload
0
citations
affunlabeled
Instance-Dependent Lower Bounds
Tor Lattimore, Csaba Szepesvári
2020· book-chapter· en· Cambridge University Press eBooks· Decision Sciences
machine prediction:candidate · noneconsensus · none
0
citations

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