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Trust, but Verify: Optimistic Visualizations of Approximate Queries for Exploring Big Data., , , and . CHI, page 2904-2915. ACM, (2017)Mining periodic behaviors for moving objects., , , , and . KDD, page 1099-1108. ACM, (2010)Collecting and Analyzing Data Jointly from Multiple Services under Local Differential Privacy., , , and . Proc. VLDB Endow., 13 (11): 2760-2772 (2020)FS-Real: A Real-World Cross-Device Federated Learning Platform., , , , , , , and . Proc. VLDB Endow., 16 (12): 4046-4049 (2023)VolcanoML: Speeding up End-to-End AutoML via Scalable Search Space Decomposition., , , , , , , , , and 1 other author(s). Proc. VLDB Endow., 14 (11): 2167-2176 (2021)Toward Personalized Answer Generation in E-Commerce via Multi-perspective Preference Modeling., , , , and . ACM Trans. Inf. Syst., 40 (4): 87:1-87:28 (2022)Efficient Reductions and a Fast Algorithm of Maximum Weighted Independent Set., , , and . WWW, page 3930-3940. ACM / IW3C2, (2021)One Size Does Not Fit All: A Bandit-Based Sampler Combination Framework with Theoretical Guarantees., , , , and . SIGMOD Conference, page 531-544. ACM, (2022)VF-PS: How to Select Important Participants in Vertical Federated Learning, Efficiently and Securely?, , , , , , , and . NeurIPS, (2022)Learning to Mutate with Hypergradient Guided Population., , , , , and . NeurIPS, (2020)