Chirag Agarwal

CoRR, 2024

Faithfulness vs. Plausibility: On the (Un)Reliability of Explanations from Large Language Models.

[BibT_eX]

[DOI]

Sree Harsha Tanneru

CoRR, 2024

2023

Quantifying Uncertainty in Natural Language Explanations of Large Language Models.

[BibT_eX]

[DOI]

Sree Harsha Tanneru

CoRR, 2023

Are Large Language Models Post Hoc Explainers?

[BibT_eX]

[DOI]

CoRR, 2023

On the Trade-offs between Adversarial Robustness and Actionable Explanations.

[BibT_eX]

[DOI]

Satyapriya Krishna

Shripad Vilasrao Deshmukh

CoRR, 2023

Certifying LLM Safety against Adversarial Prompting.

[BibT_eX]

[DOI]

CoRR, 2023

Counterfactual Explanation Policies in RL.

[BibT_eX]

[DOI]

Srivatsan R

Supriti Vijay

Jayakumar Subramanian

Vineeth N. Balasubramanian

CoRR, 2023

Towards Estimating Transferability using Hard Subsets.

[BibT_eX]

[DOI]

Shripad Vilasrao Deshmukh

Mausoom Sarkar

CoRR, 2023

Explain Like I am BM25: Interpreting a Dense Model's Ranked-List with a Sparse Approximation.

[BibT_eX]

[DOI]

Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2023

Explaining RL Decisions with Trajectories.

[BibT_eX]

[DOI]

Jayakumar Subramanian

Proceedings of the Eleventh International Conference on Learning Representations, 2023

GNNDelete: A General Strategy for Unlearning in Graph Neural Networks.

[BibT_eX]

[DOI]

Proceedings of the Eleventh International Conference on Learning Representations, 2023

DeAR: Debiasing Vision-Language Models with Additive Residuals.

[BibT_eX]

[DOI]

Ashish Seth

Mayur Hemani

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023

2022

Evaluating Explainability for Graph Neural Networks.

[BibT_eX]

[DOI]

CoRR, 2022

Rethinking Stability for Attribution-based Explanations.

[BibT_eX]

[DOI]

CoRR, 2022

OpenXAI: Towards a Transparent Evaluation of Model Explanations.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Towards Training GNNs Using Explanation Directed Message Passing.

[BibT_eX]

[DOI]

Valentina Giunchiglia

Chirag Varun Shukla

Guadalupe Gonzalez

Proceedings of the Learning on Graphs Conference, 2022

Estimating Example Difficulty using Variance of Gradients.

[BibT_eX]

[DOI]

Daniel D'souza

Sara Hooker

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022

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

[BibT_eX]

[DOI]

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

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

[BibT_eX]

[DOI]

Marinka Zitnik

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

2021

A Tale Of Two Long Tails.

[BibT_eX]

[DOI]

CoRR, 2021

On the Connections between Counterfactual Explanations and Adversarial Examples.

[BibT_eX]

[DOI]

CoRR, 2021

Towards a Rigorous Theoretical Analysis and Evaluation of GNN Explanations.

[BibT_eX]

[DOI]

Marinka Zitnik

CoRR, 2021

Towards a unified framework for fair and stable graph representation learning.

[BibT_eX]

[DOI]

Marinka Zitnik

Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, 2021

CoroNet: a deep network architecture for enhanced identification of COVID-19 from chest x-ray images.

[BibT_eX]

[DOI]

Proceedings of the Medical Imaging 2021: Computer-Aided Diagnosis, 2021

Towards the Unification and Robustness of Perturbation and Gradient Based Explanations.

[BibT_eX]

[DOI]

Proceedings of the 38th International Conference on Machine Learning, 2021

2020

Estimating Example Difficulty using Variance of Gradients.

[BibT_eX]

[DOI]

Sara Hooker

CoRR, 2020

Intriguing generalization and simplicity of adversarially trained neural networks.

[BibT_eX]

[DOI]

Peijie Chen

CoRR, 2020

DEEP-URL: A Model-Aware Approach to Blind Deconvolution Based on Deep Unfolded Richardson-Lucy Network.

[BibT_eX]

[DOI]

Proceedings of the IEEE International Conference on Image Processing, 2020

SAM: The Sensitivity of Attribution Methods to Hyperparameters.

[BibT_eX]

[DOI]

Naman Bansal

Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020

Explaining Image Classifiers by Removing Input Features Using Generative Models.

[BibT_eX]

[DOI]

Proceedings of the Computer Vision - ACCV 2020 - 15th Asian Conference on Computer Vision, Kyoto, Japan, November 30, 2020

2019

Removing input features via a generative model to explain their attributions to classifier's decisions.

[BibT_eX]

[DOI]

CoRR, 2019

Statistical Sequential Analysis for Object-based Video Forgery Detection.

[BibT_eX]

[DOI]

Proceedings of the Media Watermarking, 2019

Improving Robustness to Adversarial Examples by Encouraging Discriminative Features.

[BibT_eX]

[DOI]

Proceedings of the 2019 IEEE International Conference on Image Processing, 2019

2018

Improving Adversarial Robustness by Encouraging Discriminative Features.

[BibT_eX]

[DOI]

CoRR, 2018

An Explainable Adversarial Robustness Metric for Deep Learning Neural Networks.

[BibT_eX]

[DOI]

CoRR, 2018

CrossEncoders: A complex neural network compression framework.

[BibT_eX]

[DOI]

Mehdi Sharifzadeh

Proceedings of the Visual Information Processing and Communication IX, Burlingame, CA, USA, 28 January 2018, 2018

Multi-class segmentation of neuronal electron microscopy images using deep learning.

[BibT_eX]

[DOI]

Nivedita Khobragade

Proceedings of the Medical Imaging 2018: Image Processing, 2018

2017

A New Parallel Message-distribution Technique for Cost-based Steganography.

[BibT_eX]

[DOI]

CoRR, 2017

CrossNets : A New Approach to Complex Learning.

[BibT_eX]

[DOI]

CoRR, 2017

Convolutional neural network steganalysis's application to steganography.

[BibT_eX]

[DOI]

Proceedings of the 2017 IEEE Visual Communications and Image Processing, 2017

Accurate segmentation of lung fields on chest radiographs using deep convolutional networks.

[BibT_eX]

[DOI]

Mohammad R. Arbabshirani

Proceedings of the Medical Imaging 2017: Image Processing, 2017

Unsupervised quantification of abdominal fat from CT images using Greedy Snakes.

[BibT_eX]

[DOI]

Ahmed H. Dallal

Mohammad R. Arbabshirani

Aalpen Patel

Gregory J. Moore

Proceedings of the Medical Imaging 2017: Image Processing, 2017

Automatic estimation of heart boundaries and cardiothoracic ratio from chest x-ray images.

[BibT_eX]

[DOI]

Ahmed H. Dallal