12 citations · 27 across the 10 of their papers we have counts for
5 papers · 1 filter
On the Trade-offs between Adversarial Robustness and Actionable Explanations
Satyapriya Krishna, Chirag Agarwal, Himabindu Lakkaraju
As machine learning models are increasingly being employed in various high-stakes settings, it becomes important to ensure that predictions of these models are not only adversarial…
Towards Training GNNs using Explanation Directed Message Passing
Valentina Giunchiglia, Chirag Varun Shukla, Guadalupe Gonzalez +1
With the increasing use of Graph Neural Networks (GNNs) in critical real-world applications, several post hoc explanation methods have been proposed to understand their predictions…
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…
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…
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…