215 citations · 406 across the 16 of their papers we have counts for
7 papers · 1 filter
Towards Evaluating Exploratory Model Building Process with AutoML Systems
Sungsoo Ray Hong, Sonia Castelo, Vito D'Orazio +6
The use of Automated Machine Learning (AutoML) systems are highly open-ended and exploratory. While rigorously evaluating how end-users interact with AutoML is crucial, establishin…
Why Shouldn't All Charts Be Scatter Plots? Beyond Precision-Driven Visualizations
Enrico Bertini, Michael Correll, Steven Franconeri
A central concept in information visualization research and practice is the notion of visual variable effectiveness, or the perceptual precision at which values are decoded given v…
Melody: Generating and Visualizing Machine Learning Model Summary to Understand Data and Classifiers Together
Gromit Yeuk-Yin Chan, Enrico Bertini, Luis Gustavo Nonato +2
With the increasing sophistication of machine learning models, there are growing trends of developing model explanation techniques that focus on only one instance (local explanatio…
Towards Ground Truth Explainability on Tabular Data
Brian Barr, Ke Xu, Claudio Silva +4
In data science, there is a long history of using synthetic data for method development, feature selection and feature engineering. Our current interest in synthetic data comes fro…
Human Factors in Model Interpretability: Industry Practices, Challenges, and Needs
Sungsoo Ray Hong, Jessica Hullman, Enrico Bertini
As the use of machine learning (ML) models in product development and data-driven decision-making processes became pervasive in many domains, people's focus on building a well-perf…
PipelineProfiler: A Visual Analytics Tool for the Exploration of AutoML Pipelines
Jorge Piazentin Ono, Sonia Castelo, Roque Lopez +3
In recent years, a wide variety of automated machine learning (AutoML) methods have been proposed to search and generate end-to-end learning pipelines. While these techniques facil…