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Crowd vs. Expert: What Can Relevance Judgment Rationales Teach Us About Assessor Disagreement?, , , , и . SIGIR, стр. 805-814. ACM, (2018)Constructing Test Collections using Multi-armed Bandits and Active Learning., , и . WWW, стр. 3158-3164. ACM, (2019)The Many Benefits of Annotator Rationales for Relevance Judgments., , , и . IJCAI, стр. 4909-4913. ijcai.org, (2017)Learning Complementary Policies for Human-AI Teams., , , , , , и . CoRR, (2023)Interpretable by Design: Wrapper Boxes Combine Neural Performance with Faithful Explanations., , и . CoRR, (2023)Same Same, But Different: Conditional Multi-Task Learning for Demographic-Specific Toxicity Detection., , , и . WWW, стр. 3689-3700. ACM, (2023)Improving Quality of Crowdsourced Labels via Probabilistic Matrix Factorization., и . HCOMP@AAAI, том WS-12-08 из AAAI Technical Report, AAAI Press, (2012)Learning a neural Pareto manifold extractor with constraints., , , и . UAI, том 180 из Proceedings of Machine Learning Research, стр. 749-758. PMLR, (2022)Aggregating Complex Annotations via Merging and Matching., и . KDD, стр. 86-94. ACM, (2021)Incorporating Relevance and Pseudo-relevance Feedback in the Markov Random Field Model.. TREC, том 500-277 из NIST Special Publication, National Institute of Standards and Technology (NIST), (2008)