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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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Reinforcement Learning in Robotics
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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.

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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.

1,145 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.
1,145 works in the cohort · of 4,299,418page 10 of 23

Labels cover 2 of 1,145 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 1,145 of 1,145 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
Solving Common-Payoff Games with Approximate Policy Iteration
Samuel Sokota, Edward Lockhart, Finbarr Timbers, Elnaz Davoodi, Ryan D'Orazio, Neil Burch +3 more
2021· article· en· Proceedings of the AAAI Conference on Artificial Intelligence· Computer Science
machine prediction:candidate · noneconsensus · none
6
citations
affunlabeled
Off-policy Learning with Options and Recognizers
Doina Precup, Cosmin Păduraru, Anna Koop, Richard S. Sutton, Satinder Singh
2005· article· en· Neural Information Processing Systems· Computer Science
machine prediction:candidate · noneconsensus · none
6
citations
affunlabeled
Learning Expected Emphatic Traces for Deep RL
Ray Jiang, Shangtong Zhang, Veronica Chelu, Adam White, Hado van Hasselt
2022· article· en· Proceedings of the AAAI Conference on Artificial Intelligence· Computer Science
machine prediction:candidate · noneconsensus · none
6
citations
affunlabeled
Diversity-Enriched Option-Critic
Anand Kamat, Doina Precup
2020· preprint· en· arXiv (Cornell University)· Computer Science
machine prediction:candidate · noneconsensus · none
6
citations
afffundunlabeled
Statechart-Based AI in Practice
Christopher Dragert, Jörg Kienzle, Clark Verbrugge
2012· article· en· Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment· Computer Science
machine prediction:candidate · noneconsensus · none
6
citations
affunlabeled
MULTIAGENT EXPEDITION WITH GRAPHICAL MODELS
Yang Xiang, Frank Hanshar
2011· article· en· International Journal of Uncertainty Fuzziness and Knowledge-Based Systems· Computer Science
machine prediction:candidate · noneconsensus · none
6
citations
affunlabeled
Planning with Expectation Models
Yi Wan, Muhammad Zaheer, Adam White, Martha White, Richard S. Sutton
2019· article· en· Computer Science
machine prediction:candidate · noneconsensus · none
5
citations
affunlabeled
Learning Options with Interest Functions
Khimya Khetarpal, Doina Precup
2019· article· en· Proceedings of the AAAI Conference on Artificial Intelligence· Computer Science
machine prediction:candidate · noneconsensus · none
5
citations
affunlabeled
Continuous-Time Fitted Value Iteration for Robust Policies
Michael Lutter, Boris Belousov, Shie Mannor, Dieter Fox, Animesh Garg, Jan Peters
2022· article· en· IEEE Transactions on Pattern Analysis and Machine Intelligence· Computer Science
machine prediction:candidate · noneconsensus · none
5
citations
affunlabeled
Learning With Options That Terminate Off-Policy
Anna Harutyunyan, Peter Vrancx, Pierre‐Luc Bacon, Doina Precup, Ann Nowé
2018· preprint· en· Proceedings of the AAAI Conference on Artificial Intelligence· Computer Science
machine prediction:candidate · noneconsensus · none
5
citations
afffundunlabeled
How RL Agents Behave When Their Actions Are Modified
Eric Langlois, Tom Everitt
2021· preprint· en· Proceedings of the AAAI Conference on Artificial Intelligence· Computer Science
machine prediction:candidate · noneconsensus · none
5
citations
fundno affunlabeled
Neural Episodic Control with State Abstraction
Zhuo Li, Derui Zhu, Yujing Hu, Xiaofei Xie, Lei Ma, Yan Zheng +3 more
2023· preprint· en· arXiv (Cornell University)· Computer Science
machine prediction:candidate · noneconsensus · none
5
citations

How this was built: Screen · Findings · About