Doris Xin
Cited by
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Mllib: Machine learning in apache spark
X Meng, J Bradley, B Yavuz, E Sparks, S Venkataraman, D Liu, ...
Journal of Machine Learning Research 17 (34), 1-7, 2016
Accelerating human-in-the-loop machine learning: Challenges and opportunities
D Xin, L Ma, J Liu, S Macke, S Song, A Parameswaran
Proceedings of the Second Workshop on Data Management for End-To-End Machine …, 2018
Laser: A scalable response prediction platform for online advertising
D Agarwal, B Long, J Traupman, D Xin, L Zhang
Proceedings of the 7th ACM international conference on Web search and data …, 2014
Towards scalable dataframe systems
D Petersohn, S Macke, D Xin, W Ma, D Lee, X Mo, JE Gonzalez, ...
arXiv preprint arXiv:2001.00888, 2020
Whither automl? understanding the role of automation in machine learning workflows
D Xin, EY Wu, DJL Lee, N Salehi, A Parameswaran
Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems …, 2021
A Human-in-the-loop Perspective on AutoML: Milestones and the Road Ahead
DJL Lee, S Macke, D Xin
IEEE Data Engineering Bulletin, 2020
Extending relational query processing with ML inference
K Karanasos, M Interlandi, D Xin, F Psallidas, R Sen, K Park, I Popivanov, ...
arXiv preprint arXiv:1911.00231, 2019
Helix: Holistic optimization for accelerating iterative machine learning
D Xin, S Macke, L Ma, J Liu, S Song, A Parameswaran
arXiv preprint arXiv:1812.05762, 2018
Production machine learning pipelines: Empirical analysis and optimization opportunities
D Xin, H Miao, A Parameswaran, N Polyzotis
Proceedings of the 2021 international conference on management of data, 2639 …, 2021
Helix: accelerating human-in-the-loop machine learning [Demo]
D Xin, L Ma, J Liu, S Macke, S Song, A Parameswaran
Proceedings of the VLDB Endowment 11 (12), 1958-1961, 2018
Fine-grained lineage for safer notebook interactions
S Macke, H Gong, DJL Lee, A Head, D Xin, A Parameswaran
arXiv preprint arXiv:2012.06981, 2020
Parallel computation using active self-assembly
M Chen, D Xin, D Woods
Natural Computing 14, 225-250, 2015
How Developers Iterate on Machine Learning Workflows--A Survey of the Applied Machine Learning Literature
D Xin, L Ma, S Song, A Parameswaran
arXiv preprint arXiv:1803.10311, 2018
Enhancing the interactivity of dataframe queries by leveraging think time
D Xin, D Petersohn, D Tang, Y Wu, JE Gonzalez, JM Hellerstein, ...
arXiv preprint arXiv:2103.02145, 2021
Folding: Why good models sometimes make spurious recommendations
D Xin, N Mayoraz, H Pham, K Lakshmanan, JR Anderson
Proceedings of the Eleventh ACM Conference on Recommender Systems, 201-209, 2017
Demystifying a dark art: Understanding real-world machine learning model development
A Lee, D Xin, D Lee, A Parameswaran
arXiv preprint arXiv:2005.01520, 2020
Active learning on heterogeneous information networks: A multi-armed bandit approach
D Xin, A El-Kishky, D Liao, B Norick, J Han
2018 IEEE International Conference on Data Mining (ICDM), 1350-1355, 2018
Model compilation for feature selection in statistical models
DS Xin, JD Traupman, X Meng, PT Ogilvie
US Patent App. 14/314,811, 2015
Query Processing with Machine Learning
K Karanasos, M Interlandi, F Psallidas, R Sen, K Park, I Popivanov, ...
US Patent App. 16/990,506, 2021
Dependency management during model compilation of statistical models
DS Xin, JD Traupman, X Meng, PT Ogilvie
US Patent App. 14/314,839, 2015
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