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ORCIDDEEM@ACM SIGMOD Conference 2019: Amsterdam, The Netherlands
- Sebastian Schelter, Neoklis Polyzotis, Stephan Seufert, Manasi Vartak:
Proceedings of the 3rd International Workshop on Data Management for End-to-End Machine Learning, DEEM@SIGMOD 2019, Amsterdam, The Netherlands, June 30, 2019. ACM 2019, ISBN 978-1-4503-6797-4 - Sahaana Suri, Peter Bailis:
DROP: A Workload-Aware Optimizer for Dimensionality Reduction. 1:1-1:10 - Justin Chen
, Edward Gan, Kexin Rong, Sahaana Suri, Peter Bailis:
CrossTrainer: Practical Domain Adaptation with Loss Reweighting. 2:1-2:10 - Raoni Lourenço, Juliana Freire, Dennis E. Shasha:
Debugging Machine Learning Pipelines. 3:1-3:10 - Eran Bringer, Abraham Israeli, Yoav Shoham, Alexander Ratner, Christopher Ré:
Osprey: Weak Supervision of Imbalanced Extraction Problems without Code. 4:1-4:11 - Ki Hyun Tae, Yuji Roh, Young Hun Oh, Hyunsu Kim, Steven Euijong Whang:
Data Cleaning for Accurate, Fair, and Robust Models: A Big Data - AI Integration Approach. 5:1-5:4 - Supun Nakandala, Yuhao Zhang, Arun Kumar:
Cerebro: Efficient and Reproducible Model Selection on Deep Learning Systems. 6:1-6:4 - Maximilian E. Schüle, Matthias Bungeroth, Alfons Kemper, Stephan Günnemann, Thomas Neumann:
MLearn: A Declarative Machine Learning Language for Database Systems. 7:1-7:4 - Gharib Gharibi, Vijay Walunj, Rakan Alanazi, Sirisha Rella, Yugyung Lee:
Automated Management of Deep Learning Experiments. 8:1-8:4 - Pablo Barceló, Nelson Higuera, Jorge Pérez, Bernardo Subercaseaux:
Expressiveness of Matrix and Tensor Query Languages in terms of ML Operators. 9:1-9:5 - Vijay S. Kumar, Paul Cuddihy, Kareem S. Aggour:
NodeGroup: A Knowledge-driven Data Management Abstraction for Industrial Machine Learning. 10:1-10:4 - Vraj Shah, Arun Kumar:
The ML Data Prep Zoo: Towards Semi-Automatic Data Preparation for ML. 11:1-11:4
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