16 citations · 35 across the 5 of their papers we have counts for
8 papers
Fine-Grained Lineage for Safer Notebook Interactions
Stephen Macke, Hongpu Gong, Doris Jung-Lin Lee +3
Computational notebooks have emerged as the platform of choice for data science and analytical workflows, enabling rapid iteration and exploration. By keeping intermediate program…
Rapid Approximate Aggregation with Distribution-Sensitive Interval Guarantees
Stephen Macke, Maryam Aliakbarpour, Ilias Diakonikolas +2
Aggregating data is fundamental to data analytics, data exploration, and OLAP. Approximate query processing (AQP) techniques are often used to accelerate computation of aggregates…
Towards Scalable Dataframe Systems
Devin Petersohn, Stephen Macke, Doris Xin +7
Dataframes are a popular abstraction to represent, prepare, and analyze data. Despite the remarkable success of dataframe libraries in Rand Python, dataframes face performance issu…
Helix: Holistic Optimization for Accelerating Iterative Machine Learning
Doris Xin, Stephen Macke, Litian Ma +3
Machine learning workflow development is a process of trial-and-error: developers iterate on workflows by testing out small modifications until the desired accuracy is achieved. Un…
Helix: Accelerating Human-in-the-loop Machine Learning
Doris Xin, Litian Ma, Jialin Liu +3
Data application developers and data scientists spend an inordinate amount of time iterating on machine learning (ML) workflows -- by modifying the data pre-processing, model train…
Accelerating Human-in-the-loop Machine Learning: Challenges and Opportunities
Doris Xin, Litian Ma, Jialin Liu +3
Development of machine learning (ML) workflows is a tedious process of iterative experimentation: developers repeatedly make changes to workflows until the desired accuracy is atta…