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PlantCare: An Investigation in Practical Ubiquitous Systems.

, , , , , , , and . UbiComp, volume 2498 of Lecture Notes in Computer Science, page 316-332. Springer, (2002)

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