| 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 | 2507 | 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 | 174 | 2018 |
| 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 | 124 | 2021 |
| 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 | 122 | 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 | 120 | 2020 |
| A Human-in-the-loop Perspective on AutoML: Milestones and the Road Ahead DJL Lee, S Macke, D Xin IEEE Data Engineering Bulletin, 2020 | 93 | 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 | 89 | 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 | 81 | 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 | 78 | 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 | 44* | 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 | 40 | 2020 |
| Parallel computation using active self-assembly M Chen, D Xin, D Woods Natural Computing 14, 225-250, 2015 | 25 | 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 | 22 | 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 | 12 | 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 | 12 | 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 | 9 | 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 | 9 | 2018 |
| Model compilation for feature selection in statistical models DS Xin, JD Traupman, X Meng, PT Ogilvie US Patent App. 14/314,811, 2015 | 9 | 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 | 7 | 2021 |
| Dependency management during model compilation of statistical models DS Xin, JD Traupman, X Meng, PT Ogilvie US Patent App. 14/314,839, 2015 | 6 | 2015 |
Aditya ParameswaranAssociate Professor, UC BerkeleyVerified email at berkeley.edu