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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)Vertex-weighted Online Stochastic Matching with Patience Constraints., , and . CoRR, (2019)Characterizing Properties and Trade-offs of Centralized Delegation Mechanisms in Liquid Democracy., , and . FAccT, page 1629-1638. ACM, (2022)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).It's COMPASlicated: The Messy Relationship between RAI Datasets and Algorithmic Fairness Benchmarks., , , , , , , and . NeurIPS Datasets and Benchmarks, (2021)Improved Approximation Algorithms for Stochastic-Matching Problems., , , , , and . CoRR, (2020)Attenuate Locally, Win Globally: An Attenuation-based Framework for Online Stochastic Matching with Timeouts., , , and . CoRR, (2018)Approximation Algorithms for Radius-Based, Two-Stage Stochastic Clustering Problems with Budget Constraints., , , , , and . CoRR, (2020)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)