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Studying Early Decision Making with Progressive Bar Charts.

, , , , and . IEEE Trans. Vis. Comput. Graph., 29 (1): 407-417 (2023)

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Studying Early Decision Making with Progressive Bar Charts., , , , and . IEEE Trans. Vis. Comput. Graph., 29 (1): 407-417 (2023)Finding the most interesting correlations in a database: how hard can it be?. Information Systems, Volume 30 (1): 21-46 (March 2005)A comparison of platforms for implementing and running very large scale machine learning algorithms., , , , , and . SIGMOD Conference, page 1371-1382. ACM, (2014)Scalable Linear Algebra on a Relational Database System., , , , and . ICDE, page 523-534. IEEE Computer Society, (2017)Mixture models for learning low-dimensional roles in high-dimensional data., , and . KDD, page 909-918. ACM, (2010)History-aware query optimization with materialized intermediate views., and . ICDE, page 520-531. IEEE Computer Society, (2014)Do Anesthesiologists Know What They Are Doing? Mining a Surgical Time-Series Database to Correlate Expert Assessment with Outcomes., , , and . ACM Trans. Knowl. Discov. Data, 10 (3): 24:1-24:27 (2016)Materialized Sample Views for Database Approximation., and . IEEE Trans. Knowl. Data Eng., 20 (3): 337-351 (2008)Parallel and Distributed MCMC via Shepherding Distributions., and . AISTATS, volume 84 of Proceedings of Machine Learning Research, page 1819-1827. PMLR, (2018)ResIST: Layer-wise decomposition of ResNets for distributed training., , , and . UAI, volume 180 of Proceedings of Machine Learning Research, page 610-620. PMLR, (2022)