17 citations · 35 across the 6 of their papers we have counts for
6 papers · 1 filter
iSEA: An Interactive Pipeline for Semantic Error Analysis of NLP Models
Jun Yuan, Jesse Vig, Nazneen Rajani
Error analysis in NLP models is essential to successful model development and deployment. One common approach for diagnosing errors is to identify subpopulations in the dataset whe…
Visual Exploration of Machine Learning Model Behavior with Hierarchical Surrogate Rule Sets
Jun Yuan, Brian Barr, Kyle Overton +1
One of the potential solutions for model interpretation is to train a surrogate model: a more transparent model that approximates the behavior of the model to be explained. Typical…
An Exploration And Validation of Visual Factors in Understanding Classification Rule Sets
Jun Yuan, Oded Nov, Enrico Bertini
Rule sets are often used in Machine Learning (ML) as a way to communicate the model logic in settings where transparency and intelligibility are necessary. Rule sets are typically…
AdViCE: Aggregated Visual Counterfactual Explanations for Machine Learning Model Validation
Oscar Gomez, Steffen Holter, Jun Yuan +1
Rapid improvements in the performance of machine learning models have pushed them to the forefront of data-driven decision-making. Meanwhile, the increased integration of these mod…
Visualizing Rule Sets: Exploration and Validation of a Design Space
Jun Yuan, Oded Nov, Enrico Bertini
Rule sets are often used in Machine Learning (ML) as a way to communicate the model logic in settings where transparency and intelligibility are necessary. Rule sets are typically…
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…