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Measuring disparate outcomes of content recommendation algorithms with distributional inequality metrics.

T. Lazovich, L. Belli, A. Gonzales, A. Bower, U. Tantipongpipat, K. Lum, F. Huszár, and R. Chowdhury. Patterns, 3 (8): 100568 (2022)

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Other publications of authors with the same name

Maximizing Determinants under Matroid Constraints.V. Madan, A. Nikolov, M. Singh, and U. Tantipongpipat. FOCS, page 565-576. IEEE, (2020)Multi-Criteria Dimensionality Reduction with Applications to Fairness.U. Tantipongpipat, S. Samadi, M. Singh, J. Morgenstern, and S. Vempala. NeurIPS, page 15135-15145. (2019)Combinatorial Algorithms for Optimal Design.V. Madan, M. Singh, U. Tantipongpipat, and W. Xie. COLT, volume 99 of Proceedings of Machine Learning Research, page 2210-2258. PMLR, (2019)Fair and Diverse Data Representation in Machine Learning.U. Tantipongpipat. Georgia Institute of Technology, Atlanta, GA, USA, (2020)base-search.net (ftgeorgiatech:oai:smartech.gatech.edu:1853/63581).Fast and Memory Efficient Differentially Private-SGD via JL Projections.Z. Bu, S. Gopi, J. Kulkarni, Y. Lee, J. Shen, and U. Tantipongpipat. NeurIPS, page 19680-19691. (2021)Measuring disparate outcomes of content recommendation algorithms with distributional inequality metrics.T. Lazovich, L. Belli, A. Gonzales, A. Bower, U. Tantipongpipat, K. Lum, F. Huszár, and R. Chowdhury. Patterns, 4 (8): 100822 (August 2023)Measuring disparate outcomes of content recommendation algorithms with distributional inequality metrics.T. Lazovich, L. Belli, A. Gonzales, A. Bower, U. Tantipongpipat, K. Lum, F. Huszár, and R. Chowdhury. Patterns, 3 (8): 100568 (2022)Image Cropping on Twitter: Fairness Metrics, their Limitations, and the Importance of Representation, Design, and Agency.K. Yee, U. Tantipongpipat, and S. Mishra. CoRR, (2021)Differentially Private Mixed-Type Data Generation For Unsupervised Learning.U. Tantipongpipat, C. Waites, D. Boob, A. Siva, and R. Cummings. CoRR, (2019)A Combinatorial Approach to Ebert's Hat Game with Many Colors.U. Tantipongpipat. Electron. J. Comb., 21 (4): 4 (2014)

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Measuring disparate outcomes of content recommendation algorithms with distributional inequality metrics.

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copydeleteadd this publication to your clipboardcommunity posthistory of this postURLDOIBibTeXEndNoteAPAChicagoDIN 1505HarvardMSOffice XML Measuring disparate outcomes of content recommendation algorithms with distributional inequality metrics.

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Measuring disparate outcomes of content recommendation algorithms with distributional inequality metrics.