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  2022 (2)
DEEM'22: Data Management for End-to-End Machine Learning. Boehm, M.; Varma, P.; and Xin, D. In Ives, Z. G.; Bonifati, A.; and Abbadi, A. E., editor(s), SIGMOD '22: International Conference on Management of Data, Philadelphia, PA, USA, June 12 - 17, 2022, pages 2548–2549, 2022. ACM
DEEM'22: Data Management for End-to-End Machine Learning [link]Paper   doi   link   bibtex  
DEEM '22: Proceedings of the Sixth Workshop on Data Management for End-To-End Machine Learning Philadelphia, PA, USA, 12 June 2022. Boehm, M.; Varma, P.; and Xin, D., editors. ACM. 2022.
DEEM '22: Proceedings of the Sixth Workshop on Data Management for End-To-End Machine Learning Philadelphia, PA, USA, 12 June 2022 [link]Paper   doi   link   bibtex  
  2021 (8)
Usable and Efficient Systems for Machine Learning. Xin, D. Ph.D. Thesis, University of California, Berkeley, USA, 2021.
Usable and Efficient Systems for Machine Learning [link]Paper   link   bibtex  
Enhancing the Interactivity of Dataframe Queries by Leveraging Think Time. Xin, D.; Petersohn, D.; Tang, D.; Wu, Y.; Gonzalez, J. E.; Hellerstein, J. M.; Joseph, A. D.; and Parameswaran, A. G. IEEE Data Eng. Bull., 44(1): 66–78. 2021.
Enhancing the Interactivity of Dataframe Queries by Leveraging Think Time [pdf]Paper   link   bibtex   2 downloads  
Fine-Grained Lineage for Safer Notebook Interactions. Macke, S.; Parameswaran, A. G.; Gong, H.; Lee, D. J. L.; Xin, D.; and Head, A. Proc. VLDB Endow., 14(6): 1093–1101. 2021.
Fine-Grained Lineage for Safer Notebook Interactions [pdf]Paper   doi   link   bibtex  
Whither AutoML? Understanding the Role of Automation in Machine Learning Workflows. Xin, D.; Wu, E. Y.; Lee, D. J. L.; Salehi, N.; and Parameswaran, A. G. In Kitamura, Y.; Quigley, A.; Isbister, K.; Igarashi, T.; Bjørn, P.; and Drucker, S. M., editor(s), CHI '21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 83:1–83:16, 2021. ACM
Whither AutoML? Understanding the Role of Automation in Machine Learning Workflows [link]Paper   doi   link   bibtex  
Production Machine Learning Pipelines: Empirical Analysis and Optimization Opportunities. Xin, D.; Miao, H.; Parameswaran, A. G.; and Polyzotis, N. In Li, G.; Li, Z.; Idreos, S.; and Srivastava, D., editor(s), SIGMOD '21: International Conference on Management of Data, Virtual Event, China, June 20-25, 2021, pages 2639–2652, 2021. ACM
Production Machine Learning Pipelines: Empirical Analysis and Optimization Opportunities [link]Paper   doi   link   bibtex  
Whither AutoML? Understanding the Role of Automation in Machine Learning Workflows. Xin, D.; Wu, E. Y.; Lee, D. J. L.; Salehi, N.; and Parameswaran, A. G. CoRR, abs/2101.04834. 2021.
Whither AutoML? Understanding the Role of Automation in Machine Learning Workflows [link]Paper   link   bibtex  
Enhancing the Interactivity of Dataframe Queries by Leveraging Think Time. Xin, D.; Petersohn, D.; Tang, D.; Wu, Y.; Gonzalez, J. E.; Hellerstein, J. M.; Joseph, A. D.; and Parameswaran, A. G. CoRR, abs/2103.02145. 2021.
Enhancing the Interactivity of Dataframe Queries by Leveraging Think Time [link]Paper   link   bibtex   2 downloads  
Production Machine Learning Pipelines: Empirical Analysis and Optimization Opportunities. Xin, D.; Miao, H.; Parameswaran, A. G.; and Polyzotis, N. CoRR, abs/2103.16007. 2021.
Production Machine Learning Pipelines: Empirical Analysis and Optimization Opportunities [link]Paper   link   bibtex  
  2020 (5)
Towards Scalable Dataframe Systems. Petersohn, D.; Ma, W. W.; Lee, D. J. L.; Macke, S.; Xin, D.; Mo, X.; Gonzalez, J.; Hellerstein, J. M.; Joseph, A. D.; and Parameswaran, A. G. Proc. VLDB Endow., 13(11): 2033–2046. 2020.
Towards Scalable Dataframe Systems [pdf]Paper   link   bibtex   7 downloads  
Extending Relational Query Processing with ML Inference. Karanasos, K.; Interlandi, M.; Psallidas, F.; Sen, R.; Park, K.; Popivanov, I.; Xin, D.; Nakandala, S.; Krishnan, S.; Weimer, M.; Yu, Y.; Ramakrishnan, R.; and Curino, C. In 10th Conference on Innovative Data Systems Research, CIDR 2020, Amsterdam, The Netherlands, January 12-15, 2020, Online Proceedings, 2020. www.cidrdb.org
Extending Relational Query Processing with ML Inference [pdf]Paper   link   bibtex  
Towards Scalable Dataframe Systems. Petersohn, D.; Ma, W. W.; Lee, D. J. L.; Macke, S.; Xin, D.; Mo, X.; Gonzalez, J. E.; Hellerstein, J. M.; Joseph, A. D.; and Parameswaran, A. G. CoRR, abs/2001.00888. 2020.
Towards Scalable Dataframe Systems [link]Paper   link   bibtex   7 downloads  
Demystifying a Dark Art: Understanding Real-World Machine Learning Model Development. Lee, A.; Xin, D.; Lee, D.; and Parameswaran, A. G. CoRR, abs/2005.01520. 2020.
Demystifying a Dark Art: Understanding Real-World Machine Learning Model Development [link]Paper   link   bibtex  
Fine-Grained Lineage for Safer Notebook Interactions. Macke, S.; Gong, H.; Lee, D. J. L.; Head, A.; Xin, D.; and Parameswaran, A. G. CoRR, abs/2012.06981. 2020.
Fine-Grained Lineage for Safer Notebook Interactions [link]Paper   link   bibtex  
  2019 (2)
A Human-in-the-loop Perspective on AutoML: Milestones and the Road Ahead. Lee, D. J. L.; Macke, S.; Xin, D.; Lee, A.; Huang, S.; and Parameswaran, A. G. IEEE Data Eng. Bull., 42(2): 59–70. 2019.
A Human-in-the-loop Perspective on AutoML: Milestones and the Road Ahead [pdf]Paper   link   bibtex  
Extending Relational Query Processing with ML Inference. Karanasos, K.; Interlandi, M.; Xin, D.; Psallidas, F.; Sen, R.; Park, K.; Popivanov, I.; Nakandala, S.; Krishnan, S.; Weimer, M.; Yu, Y.; Ramakrishnan, R.; and Curino, C. CoRR, abs/1911.00231. 2019.
Extending Relational Query Processing with ML Inference [link]Paper   link   bibtex  
  2018 (8)
Helix: Accelerating Human-in-the-loop Machine Learning. Xin, D.; Ma, L.; Liu, J.; Macke, S.; Song, S.; and Parameswaran, A. G. Proc. VLDB Endow., 11(12): 1958–1961. 2018.
Helix: Accelerating Human-in-the-loop Machine Learning [pdf]Paper   doi   link   bibtex  
Helix: Holistic Optimization for Accelerating Iterative Machine Learning. Xin, D.; Macke, S.; Ma, L.; Liu, J.; Song, S.; and Parameswaran, A. G. Proc. VLDB Endow., 12(4): 446–460. 2018.
Helix: Holistic Optimization for Accelerating Iterative Machine Learning [pdf]Paper   doi   link   bibtex  
Active Learning on Heterogeneous Information Networks: A Multi-armed Bandit Approach. Xin, D.; El-Kishky, A.; Liao, D.; Norick, B.; and Han, J. In IEEE International Conference on Data Mining, ICDM 2018, Singapore, November 17-20, 2018, pages 1350–1355, 2018. IEEE Computer Society
Active Learning on Heterogeneous Information Networks: A Multi-armed Bandit Approach [link]Paper   doi   link   bibtex  
Accelerating Human-in-the-loop Machine Learning: Challenges and Opportunities. Xin, D.; Ma, L.; Liu, J.; Macke, S.; Song, S.; and Parameswaran, A. G. In Schelter, S.; Seufert, S.; and Kumar, A., editor(s), Proceedings of the Second Workshop on Data Management for End-To-End Machine Learning, DEEM@SIGMOD 2018, Houston, TX, USA, June 15, 2018, pages 9:1–9:4, 2018. ACM
Accelerating Human-in-the-loop Machine Learning: Challenges and Opportunities [link]Paper   doi   link   bibtex  
How Developers Iterate on Machine Learning Workflows - A Survey of the Applied Machine Learning Literature. Xin, D.; Ma, L.; Song, S.; and Parameswaran, A. G. CoRR, abs/1803.10311. 2018.
How Developers Iterate on Machine Learning Workflows - A Survey of the Applied Machine Learning Literature [link]Paper   link   bibtex  
Accelerating Human-in-the-loop Machine Learning: Challenges and Opportunities. Xin, D.; Ma, L.; Liu, J.; Macke, S.; Song, S.; and Parameswaran, A. G. CoRR, abs/1804.05892. 2018.
Accelerating Human-in-the-loop Machine Learning: Challenges and Opportunities [link]Paper   link   bibtex  
Helix: Accelerating Human-in-the-loop Machine Learning. Xin, D.; Ma, L.; Liu, J.; Macke, S.; Song, S.; and Parameswaran, A. G. CoRR, abs/1808.01095. 2018.
Helix: Accelerating Human-in-the-loop Machine Learning [link]Paper   link   bibtex  
Helix: Holistic Optimization for Accelerating Iterative Machine Learning. Xin, D.; Macke, S.; Ma, L.; Liu, J.; Song, S.; and Parameswaran, A. G. CoRR, abs/1812.05762. 2018.
Helix: Holistic Optimization for Accelerating Iterative Machine Learning [link]Paper   link   bibtex  
  2017 (1)
Folding: Why Good Models Sometimes Make Spurious Recommendations. Xin, D.; Mayoraz, N.; Pham, H.; Lakshmanan, K.; and Anderson, J. R. In Cremonesi, P.; Ricci, F.; Berkovsky, S.; and Tuzhilin, A., editor(s), Proceedings of the Eleventh ACM Conference on Recommender Systems, RecSys 2017, Como, Italy, August 27-31, 2017, pages 201–209, 2017. ACM
Folding: Why Good Models Sometimes Make Spurious Recommendations [link]Paper   doi   link   bibtex  
  2016 (1)
MLlib: Machine Learning in Apache Spark. Meng, X.; Bradley, J. K.; Yavuz, B.; Sparks, E. R.; Venkataraman, S.; Liu, D.; Freeman, J.; Tsai, D. B.; Amde, M.; Owen, S.; Xin, D.; Xin, R.; Franklin, M. J.; Zadeh, R.; Zaharia, M.; and Talwalkar, A. J. Mach. Learn. Res., 17: 34:1–34:7. 2016.
MLlib: Machine Learning in Apache Spark [link]Paper   link   bibtex  
  2015 (2)
Parallel computation using active self-assembly. Chen, M.; Xin, D.; and Woods, D. Nat. Comput., 14(2): 225–250. 2015.
Parallel computation using active self-assembly [link]Paper   doi   link   bibtex  
MLlib: Machine Learning in Apache Spark. Meng, X.; Bradley, J. K.; Yavuz, B.; Sparks, E. R.; Venkataraman, S.; Liu, D.; Freeman, J.; Tsai, D. B.; Amde, M.; Owen, S.; Xin, D.; Xin, R.; Franklin, M. J.; Zadeh, R.; Zaharia, M.; and Talwalkar, A. CoRR, abs/1505.06807. 2015.
MLlib: Machine Learning in Apache Spark [link]Paper   link   bibtex  
  2014 (2)
LASER: a scalable response prediction platform for online advertising. Agarwal, D.; Long, B.; Traupman, J.; Xin, D.; and Zhang, L. In Carterette, B.; Diaz, F.; Castillo, C.; and Metzler, D., editor(s), Seventh ACM International Conference on Web Search and Data Mining, WSDM 2014, New York, NY, USA, February 24-28, 2014, pages 173–182, 2014. ACM
LASER: a scalable response prediction platform for online advertising [link]Paper   doi   link   bibtex  
Parallel computation using active self-assembly. Chen, M.; Xin, D.; and Woods, D. CoRR, abs/1405.0527. 2014.
Parallel computation using active self-assembly [link]Paper   link   bibtex  
  2013 (1)
Parallel Computation Using Active Self-assembly. Chen, M.; Xin, D.; and Woods, D. In Soloveichik, D.; and Yurke, B., editor(s), DNA Computing and Molecular Programming - 19th International Conference, DNA 19, Tempe, AZ, USA, September 22-27, 2013. Proceedings, volume 8141, of Lecture Notes in Computer Science, pages 16–30, 2013. Springer
Parallel Computation Using Active Self-assembly [link]Paper   doi   link   bibtex