Place Your Bets: Will Machine Learning Outgrow Human Labeling? von Schaekermann, Mike, Homan, Christopher M., Aroyo, Lora, Paritosh, Praveen, Bollacker, Kurt, Welty, Chris

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Dichotomy results for fixed point counting in boolean dynamical systems von Homan, Christopher M., Kosub, Sven

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Connecting the invisible dots: Reaching lesbian, gay, and bisexual adolescents and young adults at risk for suicide through online social networks von Silenzio, Vincent M.B., Duberstein, Paul R., Tang, Wan, Lu, Naiji, Tu, Xin, Homan, Christopher M.

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On the approximability of Dodgson and Young elections von Caragiannis, Ioannis, Covey, Jason A., Feldman, Michal, Homan, Christopher M., Kaklamanis, Christos, Karanikolas, Nikos, Procaccia, Ariel D., Rosenschein, Jeffrey S.

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Guarantees for the success frequency of an algorithm for finding Dodgson-election winners von Homan, Christopher M., Hemaspaandra, Lane A.

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The complexity of computing the size of an interval von HEMASPAANDRA, Lane A, HOMAN, Christopher M, KOSUB, Sven, WAGNER, Klaus W

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Guarantees for the Success Frequency of an Algorithm for Finding Dodgson-Election Winners von Homan, Christopher M., Hemaspaandra, Lane A.

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Cluster Computing and the Power of Edge Recognition von Hemaspaandra, Lane A., Homan, Christopher M., Kosub, Sven

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Understanding Discourse on Work and Job-Related Well-Being in Public Social Media von Liu, Tong, Homan, Christopher M, Alm, Cecilia Ovesdotter, White, Ann Marie, Lytle, Megan C, Kautz, Henry A

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Connecting the Invisible Dots: Network-Based Methods to Reach a Hidden Population at Risk for Suicide von Silenzio, Vincent Michael Bernard, Duberstein, Paul R, Tu, Xin, Tang, Wan, Lu, Naiji, Homan, Christopher M

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ARTICLE: Annotator Reliability Through In-Context Learning von Dutta, Sujan, Pandita, Deepak, Weerasooriya, Tharindu Cyril, Zampieri, Marcos, Homan, Christopher M, KhudaBukhsh, Ashiqur R

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Tight lower bounds on the ambiguity of strong, total, associative, one-way functions von Homan, Christopher M.

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Twitter Job/Employment Corpus: A Dataset of Job-Related Discourse Built with Humans in the Loop von Liu, Tong, Homan, Christopher M

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Rater Cohesion and Quality from a Vicarious Perspective von Pandita, Deepak, Weerasooriya, Tharindu Cyril, Dutta, Sujan, Luger, Sarah K, Ranasinghe, Tharindu, KhudaBukhsh, Ashiqur R, Zampieri, Marcos, Homan, Christopher M

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Subjective Crowd Disagreements for Subjective Data: Uncovering Meaningful CrowdOpinion with Population-level Learning von Weerasooriya, Tharindu Cyril, Luger, Sarah, Poddar, Saloni, KhudaBukhsh, Ashiqur R, Homan, Christopher M

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Cross-lingual Offensive Language Identification for Low Resource Languages: The Case of Marathi von Gaikwad, Saurabh, Ranasinghe, Tharindu, Zampieri, Marcos, Homan, Christopher M

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Respondent-Driven Sampling in Online Social Networks von Homan, Christopher M., Silenzio, Vincent, Sell, Randall

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Improving Label Quality by Jointly Modeling Items and Annotators von Weerasooriya, Tharindu Cyril, Ororbia, Alexander G, Homan, Christopher M

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LCP-RIT at SemEval-2021 Task 1: Exploring Linguistic Features for Lexical Complexity Prediction von Desai, Abhinandan, North, Kai, Zampieri, Marcos, Homan, Christopher M

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Vicarious Offense and Noise Audit of Offensive Speech Classifiers: Unifying Human and Machine Disagreement on What is Offensive von Weerasooriya, Tharindu Cyril, Dutta, Sujan, Ranasinghe, Tharindu, Zampieri, Marcos, Homan, Christopher M, KhudaBukhsh, Ashiqur R

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