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AutoML in Heavily Constrained Applications

, , and . VLDB Journal, (Nov 17, 2023)DBLP License: DBLP's bibliographic metadata records provided through http://dblp.org/ are distributed under a Creative Commons CC0 1.0 Universal Public Domain Dedication. Although the bibliographic metadata records are provided consistent with CC0 1.0 Dedication, the content described by the metadata records is not. Content may be subject to copyright, rights of privacy, rights of publicity and other restrictions..
DOI: 10.48550/arXiv.2306.16913

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AutoML in Heavily Constrained Applications., , and . CoRR, (2023)Automated Feature Engineering for Algorithmic Fairness., , and . Proc. VLDB Endow., 14 (9): 1694-1702 (2021)Data Science für alle: Grundlagen der Datenprogrammierung., , , , , and . Inform. Spektrum, 43 (2): 129-136 (2020)ED2: Two-stage Active Learning for Error Detection - Technical Report., , and . CoRR, (2019)Data Cleaning and AutoML: Would an Optimizer Choose to Clean?, , , , and . Datenbank-Spektrum, 22 (2): 121-130 (2022)ED2: A Case for Active Learning in Error Detection., , and . CIKM, page 2249-2252. ACM, (2019)From Cleaning before ML to Cleaning for ML., , , and . IEEE Data Eng. Bull., 44 (1): 24-41 (2021)HALEF: An Open-Source Standard-Compliant Telephony-Based Modular Spoken Dialog System: A Review and An Outlook., , , , and . IWSDS, page 53-61. Springer, (2015)Enforcing Constraints for Machine Learning Systems via Declarative Feature Selection: An Experimental Study., , and . SIGMOD Conference, page 1345-1358. ACM, (2021)Towards Automated Data Cleaning Workflows., , , and . LWDA, volume 2454 of CEUR Workshop Proceedings, page 10-19. CEUR-WS.org, (2019)