20 citations · 70 across the 17 of their papers we have counts for
6 papers · 1 filter
Iterative Partial Fulfillment of Counterfactual Explanations: Benefits and Risks
Yilun Zhou
Counterfactual (CF) explanations, also known as contrastive explanations and algorithmic recourses, are popular for explaining machine learning models in high-stakes domains. For a…
The Solvability of Interpretability Evaluation Metrics
Yilun Zhou, Julie Shah
Feature attribution methods are popular for explaining neural network predictions, and they are often evaluated on metrics such as comprehensiveness and sufficiency. In this paper,…
Do Feature Attribution Methods Correctly Attribute Features?
Yilun Zhou, Serena Booth, Marco Tulio Ribeiro +1
Feature attribution methods are popular in interpretable machine learning. These methods compute the attribution of each input feature to represent its importance, but there is no…
Towards Understanding the Behaviors of Optimal Deep Active Learning Algorithms
Yilun Zhou, Adithya Renduchintala, Xian Li +3
Active learning (AL) algorithms may achieve better performance with fewer data because the model guides the data selection process. While many algorithms have been proposed, there…
Bayes-TrEx: a Bayesian Sampling Approach to Model Transparency by Example
Serena Booth, Yilun Zhou, Ankit Shah +1
Post-hoc explanation methods are gaining popularity for interpreting, understanding, and debugging neural networks. Most analyses using such methods explain decisions in response t…
Sampling Prediction-Matching Examples in Neural Networks: A Probabilistic Programming Approach
Serena Booth, Ankit Shah, Yilun Zhou +1
Though neural network models demonstrate impressive performance, we do not understand exactly how these black-box models make individual predictions. This drawback has led to subst…