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Hierarchical Deep Reinforcement Learning For Robotics and Data Science.. University of California, Berkeley, USA, (2018)base-search.net (ftcdlib:qt9m67h5x2).DDCO: Discovery of Deep Continuous Options for Robot Learning from Demonstrations., , , and . CoRL, volume 78 of Proceedings of Machine Learning Research, page 418-437. PMLR, (2017)SPRK: A Low-Cost Stewart Platform For Motion Study In Surgical Robotics., , , and . CoRR, (2017)Selectivity Estimation with Deep Likelihood Models., , , , , , , , , and . CoRR, (2019)SWIRL: A SequentialWindowed Inverse Reinforcement Learning Algorithm for Robot Tasks With Delayed Rewards., , , , , , and . WAFR, volume 13 of Springer Proceedings in Advanced Robotics, page 672-687. Springer, (2016)VizExtract: Automatic Relation Extraction from Data Visualizations., and . CoRR, (2021)JanusAQP: Efficient Partition Tree Maintenance for Dynamic Approximate Query Processing., , and . CoRR, (2022)EdgeServe: An Execution Layer for Decentralized Prediction., and . CoRR, (2023)CIAO: An Optimization Framework for Client-Assisted Data Loading., , , , and . ICDE, page 1979-1984. IEEE, (2021)Hierarchical Residual Encoding for Multiresolution Time Series Compression., , , and . Proc. ACM Manag. Data, 1 (1): 99:1-99:26 (2023)