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iSEA: An Interactive Pipeline for Semantic Error Analysis of NLP Models



Jun Yuan , Jesse Vig , Nazneen Rajani

* Accepted at IUI 2022, 11 pages, 6 figures 

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Visual Exploration of Machine Learning Model Behavior with Hierarchical Surrogate Rule Sets



Jun Yuan , Brian Barr , Kyle Overton , Enrico Bertini


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An Exploration And Validation of Visual Factors in Understanding Classification Rule Sets



Jun Yuan , Oded Nov , Enrico Bertini

* arXiv admin note: substantial text overlap with arXiv:2103.01022 

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AdViCE: Aggregated Visual Counterfactual Explanations for Machine Learning Model Validation



Oscar Gomez , Steffen Holter , Jun Yuan , Enrico Bertini

* 4 pages, 2 figures, IEEE VIS 2021 Machine learning, interpretability, explainability, counterfactual explanations, data visualization 

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Maximizing the Set Cardinality of Users Scheduled for Ultra-dense uRLLC Networks



Shiwen He , Jun Yuan , Zhenyu An , Yunshan Yi , Yongming Huang

* 4 pages, 2 figures 

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Visualizing Rule Sets: Exploration and Validation of a Design Space



Jun Yuan , Oded Nov , Enrico Bertini


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Mind the Pad -- CNNs can Develop Blind Spots



Bilal Alsallakh , Narine Kokhlikyan , Vivek Miglani , Jun Yuan , Orion Reblitz-Richardson

* Appendix E available at https://drive.google.com/file/d/1bIvRQJIBwJbKTfpg0hNaFX2ThuuDO8PU/view?usp=sharing 

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ViCE: Visual Counterfactual Explanations for Machine Learning Models



Oscar Gomez , Steffen Holter , Jun Yuan , Enrico Bertini

* 4 pages, 2 figures, ACM IUI 2020 

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