8 citations · 12 across the 5 of their papers we have counts for
10 papers
ExSum: From Local Explanations to Model Understanding
Yilun Zhou, Marco Tulio Ribeiro, Julie Shah
Interpretability methods are developed to understand the working mechanisms of black-box models, which is crucial to their responsible deployment. Fulfilling this goal requires bot…
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…
RoCUS: Robot Controller Understanding via Sampling
Yilun Zhou, Serena Booth, Nadia Figueroa +1
As robots are deployed in complex situations, engineers and end users must develop a holistic understanding of their behaviors, capabilities, and limitations. Some behaviors are di…
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…
Adversarially Guided Self-Play for Adopting Social Conventions
Mycal Tucker, Yilun Zhou, Julie Shah
Robotic agents must adopt existing social conventions in order to be effective teammates. These social conventions, such as driving on the right or left side of the road, are arbit…