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Markets, Elections, and Microbes: Data-driven Algorithms from Theory to Practice.

. University of Maryland, College Park, MD, USA, (2020)base-search.net (ftunivmaryland:oai:drum.lib.umd.edu:1903/26557).

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Algorithms to Approximate Column-Sparse Packing Problems., , , and . SODA, page 311-330. SIAM, (2018)Improved Bound for Online Square-into-Square Packing.. WAOA, volume 8952 of Lecture Notes in Computer Science, page 47-58. Springer, (2014)Fair Clustering Under a Bounded Cost., , , and . NeurIPS, page 14345-14357. (2021)Follow Your Star: New Frameworks for Online Stochastic Matching with Known and Unknown Patience., , , and . AISTATS, volume 130 of Proceedings of Machine Learning Research, page 2872-2880. PMLR, (2021)Approximating Two-Stage Stochastic Supplier Problems., , , , , and . APPROX-RANDOM, volume 207 of LIPIcs, page 23:1-23:22. Schloss Dagstuhl - Leibniz-Zentrum für Informatik, (2021)Fair Labeled Clustering., , , and . KDD, page 327-335. ACM, (2022)Improved Guarantees for Offline Stochastic Matching via new Ordered Contention Resolution Schemes., , , and . NeurIPS, page 27184-27195. (2021)Algorithms to Approximate Column-Sparse Packing Problems., , , and . CoRR, (2017)Attenuate Locally, Win Globally: An Attenuation-based Framework for Online Stochastic Matching with Timeouts., , , and . AAMAS, page 1223-1231. ACM, (2017)Further Improvement in Approximating the Maximum Duo-Preservation String Mapping Problem.. WABI, volume 9838 of Lecture Notes in Computer Science, page 52-64. Springer, (2016)