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Scalable and Interpretable Data Representation for High-Dimensional, Complex Data., , , and . AAAI, page 1763-1769. AAAI Press, (2015)Corporate learning at scale: lessons from a large online course at google., , , , , , and . L@S, page 187-188. ACM, (2014)Amplifying community content creation with mixed initiative information extraction., , , , , and . CHI, page 1849-1858. ACM, (2009)Jury Learning: Integrating Dissenting Voices into Machine Learning Models., , , , , , and . CHI, page 115:1-115:19. ACM, (2022)Neo: Generalizing Confusion Matrix Visualization to Hierarchical and Multi-Output Labels., , , , , , , and . CoRR, (2021)Combining Visualization and Statistical Analysis to Improve Operator Confidence and Efficiency for Failure Detection and Localization., , , , , , , , , and 1 other author(s). ICAC, page 89-100. IEEE Computer Society, (2005)The Future of Notebook Programming Is Fluid., , , , and . CHI Extended Abstracts, page 1-8. ACM, (2020)Tempura: Query Analysis with Structural Templates., , , , and . CHI, page 1-12. ACM, (2020)Lowering the barrier to applying machine learning.. CHI Extended Abstracts, page 2907-2910. ACM, (2010)VoiceLabel: using speech to label mobile sensor data., , , , , , and . ICMI, page 69-76. ACM, (2008)