215 citations · 406 across the 16 of their papers we have counts for
6 papers · 2 filters
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…
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…
ViCE: Visual Counterfactual Explanations for Machine Learning Models
Oscar Gomez, Steffen Holter, Jun Yuan +1
The continued improvements in the predictive accuracy of machine learning models have allowed for their widespread practical application. Yet, many decisions made with seemingly ac…