43 citations · 62 across the 6 of their papers we have counts for
8 papers
Lux: Always-on Visualization Recommendations for Exploratory Dataframe Workflows
Doris Jung-Lin Lee, Dixin Tang, Kunal Agarwal +8
Exploratory data science largely happens in computational notebooks with dataframe APIs, such as pandas, that support flexible means to transform, clean, and analyze data. Yet, vis…
Deconstructing Categorization in Visualization Recommendation: A Taxonomy and Comparative Study
Doris Jung-Lin Lee, Vidya Setlur, Melanie Tory +2
Visualization recommendation (VisRec) systems provide users with suggestions for potentially interesting and useful next steps during exploratory data analysis. These recommendatio…
Whither AutoML? Understanding the Role of Automation in Machine Learning Workflows
Doris Xin, Eva Yiwei Wu, Doris Jung-Lin Lee +2
Efforts to make machine learning more widely accessible have led to a rapid increase in Auto-ML tools that aim to automate the process of training and deploying machine learning. T…
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…
Demystifying a Dark Art: Understanding Real-World Machine Learning Model Development
Angela Lee, Doris Xin, Doris Lee +1
It is well-known that the process of developing machine learning (ML) workflows is a dark-art; even experts struggle to find an optimal workflow leading to a high accuracy model. U…
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…