Micah J. Smith
Title
Cited by
Cited by
Year
Atmseer: Increasing transparency and controllability in automated machine learning
Q Wang, Y Ming, Z Jin, Q Shen, D Liu, MJ Smith, K Veeramachaneni, ...
Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems†…, 2019
422019
Query optimization for dynamic imputation
J Cambronero, JK Feser, MJ Smith, S Madden
Proceedings of the VLDB Endowment 10 (11), 1310-1321, 2017
192017
FeatureHub: Towards collaborative data science
MJ Smith, R Wedge, K Veeramachaneni
IEEE International Conference on Data Science and Advanced Analytics, 590-600, 2017
162017
The Machine Learning Bazaar: Harnessing the ML Ecosystem for Effective System Development
MJ Smith, C Sala, JM Kanter, K Veeramachaneni
Proceedings of the 2020 ACM SIGMOD International Conference on Management of†…, 2020
132020
Understanding User-Bot Interactions for Small-Scale Automation in Open-Source Development
D Liu, MJ Smith, K Veeramachaneni
Extended Abstracts of the 2020 CHI Conference on Human Factors in Computing†…, 2020
62020
Ballet: A lightweight framework for open-source, collaborative feature engineering
MJ Smith, K Lu, K Veeramachaneni
Workshop on Systems for ML and Open Source Software at NeurIPS 2018, 2018
22018
Enabling collaborative data science development with the Ballet framework
MJ Smith, J Cito, K Lu, K Veeramachaneni
arXiv preprint arXiv:2012.07816, 2020
12020
Meeting in the notebook: a notebook-based environment for micro-submissions in data science collaborations
MJ Smith, J Cito, K Veeramachaneni
arXiv preprint arXiv:2103.15787, 2021
2021
A Level-wise Taxonomic Perspective on Automated Machine Learning to Date and Beyond: Challenges and Opportunities
SK Karmaker Santu, M Hassan, MJ Smith, L Xu, CX Zhai, ...
arXiv preprint arXiv:2010.10777, 2020
2020
Scaling Collaborative Open Data Science
MJ Smith
Massachusetts Institute of Technology, 2018
2018
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