Jun Yuan

Artificial Intelligence · with Bertini · 2020

A researcher in the Science corpus: 1 paper; trusts 5 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 19 citations into it
• Safety, Robustness & Interpretability — 13 citations into it
• Computer Vision and Pattern Recognition — 6 citations into it

Whom Jun Yuan trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. David Martens
2. Scott M. Lundberg

Papers from Jun Yuan's perspective

Ordered by how strongly the researchers Jun Yuan trusts endorse each paper. Same corpus, a different order for every researcher.

1. A Unified Approach to Interpreting Model Predictions
2. A Workflow for Visual Diagnostics of Binary Classifiers using Instance-Level Explanations
3. Explaining Data-Driven Document Classifications
4. Explanation in Artificial Intelligence: Insights from the Social Sciences
5. Manifold: A Model-Agnostic Framework for Interpretation and Diagnosis of Machine Learning Models
6. RuleMatrix: Visualizing and Understanding Classifiers with Rules
7. Explaining machine learning classifiers through diverse counterfactual explanations
8. iForest: Interpreting Random Forests via Visual Analytics
9. Interpreting Black-Box Classifiers Using Instance-Level Visual Explanations
10. The What-If Tool: Interactive Probing of Machine Learning Models

Their papers

• ViCE: visual counterfactual explanations for machine learning models (2020) — 108 citations