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09/06/2023
Certifying LLM Safety against Adversarial Prompting
Large language models (LLMs) released for public use incorporate guardra...
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07/25/2023
Counterfactual Explanation Policies in RL
As Reinforcement Learning (RL) agents are increasingly employed in diver...
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05/06/2023
Explaining RL Decisions with Trajectories
Explanation is a key component for the adoption of reinforcement learnin...
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04/25/2023
Explain like I am BM25: Interpreting a Dense Model's Ranked-List with a Sparse Approximation
Neural retrieval models (NRMs) have been shown to outperform their stati...
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03/18/2023
DeAR: Debiasing Vision-Language Models with Additive Residuals
Large pre-trained vision-language models (VLMs) reduce the time for deve...
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01/17/2023
Towards Estimating Transferability using Hard Subsets
As transfer learning techniques are increasingly used to transfer knowle...
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11/30/2022
Towards Training GNNs using Explanation Directed Message Passing
With the increasing use of Graph Neural Networks (GNNs) in critical real...
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08/19/2022
Evaluating Explainability for Graph Neural Networks
As post hoc explanations are increasingly used to understand the behavio...
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06/22/2022
OpenXAI: Towards a Transparent Evaluation of Model Explanations
While several types of post hoc explanation methods (e.g., feature attri...
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03/14/2022
Rethinking Stability for Attribution-based Explanations
As attribution-based explanation methods are increasingly used to establ...
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07/27/2021
A Tale Of Two Long Tails
As machine learning models are increasingly employed to assist human dec...
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06/18/2021
On the Connections between Counterfactual Explanations and Adversarial Examples
Counterfactual explanations and adversarial examples have emerged as cri...
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06/16/2021
Towards a Rigorous Theoretical Analysis and Evaluation of GNN Explanations
As Graph Neural Networks (GNNs) are increasingly employed in real-world ...
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02/25/2021
Towards a Unified Framework for Fair and Stable Graph Representation Learning
As the representations output by Graph Neural Networks (GNNs) are increa...
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02/21/2021
Towards the Unification and Robustness of Perturbation and Gradient Based Explanations
As machine learning black boxes are increasingly being deployed in criti...
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08/26/2020
Estimating Example Difficulty using Variance of Gradients
In machine learning, a question of great interest is understanding what ...
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06/16/2020
Intriguing generalization and simplicity of adversarially trained neural networks
Adversarial training has been the topic of dozens of studies and a leadi...
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02/03/2020
Deep-URL: A Model-Aware Approach To Blind Deconvolution Based On Deep Unfolded Richardson-Lucy Network
The lack of interpretability in current deep learning models causes seri...
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10/09/2019
Removing input features via a generative model to explain their attributions to classifier's decisions
Interpretability methods often measure the contribution of an input feat...
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11/01/2018
Improving Adversarial Robustness by Encouraging Discriminative Features
Deep neural networks (DNNs) have achieved state-of-the-art results in va...
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06/05/2018
An Explainable Adversarial Robustness Metric for Deep Learning Neural Networks
Deep Neural Networks(DNN) have excessively advanced the field of compute...
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11/06/2017
Convolutional Neural Network Steganalysis's Application to Steganography
This paper presents a novel approach to increase the performance bounds ...
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05/24/2017
A New Parallel Message-distribution Technique for Cost-based Steganography
This paper presents two novel approaches to increase performance bounds ...
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05/21/2017
