4 citations · 7 across the 4 of their papers we have counts for
8 papers
Rethinking Stability for Attribution-based Explanations
Chirag Agarwal, Nari Johnson, Martin Pawelczyk +4
As attribution-based explanation methods are increasingly used to establish model trustworthiness in high-stakes situations, it is critical to ensure that these explanations are st…
A Tale Of Two Long Tails
Daniel D'souza, Zach Nussbaum, Chirag Agarwal +1
As machine learning models are increasingly employed to assist human decision-makers, it becomes critical to communicate the uncertainty associated with these model predictions. Ho…
Towards the Unification and Robustness of Perturbation and Gradient Based Explanations
Sushant Agarwal, Shahin Jabbari, Chirag Agarwal +3
As machine learning black boxes are increasingly being deployed in critical domains such as healthcare and criminal justice, there has been a growing emphasis on developing techniq…
SAM: The Sensitivity of Attribution Methods to Hyperparameters
Naman Bansal, Chirag Agarwal, Anh Nguyen
Attribution methods can provide powerful insights into the reasons for a classifier's decision. We argue that a key desideratum of an explanation method is its robustness to input…
Explaining image classifiers by removing input features using generative models
Chirag Agarwal, Anh Nguyen
Perturbation-based explanation methods often measure the contribution of an input feature to an image classifier's outputs by heuristically removing it via e.g. blurring, adding no…
Improving Adversarial Robustness by Encouraging Discriminative Features
Chirag Agarwal, Anh Nguyen, Dan Schonfeld
Deep neural networks (DNNs) have achieved state-of-the-art results in various pattern recognition tasks. However, they perform poorly on out-of-distribution adversarial examples i.…