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20172025
most citedOpenXAI: Towards a Transparent Evaluation of Model Explanations

82 citations · 161 across the 23 of their papers we have counts for

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Showing 2021Show all

5 papers · 1 filter

cs.CV2021★ 1 cited

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…

cs.LG2021

Exploring Counterfactual Explanations Through the Lens of Adversarial Examples: A Theoretical and Empirical Analysis

Martin Pawelczyk, Chirag Agarwal, Shalmali Joshi +2

As machine learning (ML) models become more widely deployed in high-stakes applications, counterfactual explanations have emerged as key tools for providing actionable model explan…

cs.LG2021

Probing GNN Explainers: A Rigorous Theoretical and Empirical Analysis of GNN Explanation Methods

Chirag Agarwal, Marinka Zitnik, Himabindu Lakkaraju

As Graph Neural Networks (GNNs) are increasingly being employed in critical real-world applications, several methods have been proposed in recent literature to explain the predicti…

cs.LG2021

Towards a Unified Framework for Fair and Stable Graph Representation Learning

Chirag Agarwal, Himabindu Lakkaraju, Marinka Zitnik

As the representations output by Graph Neural Networks (GNNs) are increasingly employed in real-world applications, it becomes important to ensure that these representations are fa…

cs.LG2021

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…