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09/17/2023
Mitigating Shortcuts in Language Models with Soft Label Encoding
Recent research has shown that large language models rely on spurious co...
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09/15/2023
Adaptive Priority Reweighing for Generalizing Fairness Improvement
With the increasing penetration of machine learning applications in crit...
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09/02/2023
Explainability for Large Language Models: A Survey
Large language models (LLMs) have demonstrated impressive capabilities i...
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08/20/2023
A Survey on Fairness in Large Language Models
Large language models (LLMs) have shown powerful performance and develop...
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08/08/2023
XGBD: Explanation-Guided Graph Backdoor Detection
Backdoor attacks pose a significant security risk to graph learning mode...
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07/14/2023
DISPEL: Domain Generalization via Domain-Specific Liberating
Domain generalization aims to learn a generalization model that can perf...
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07/04/2023
Prompt Tuning Pushes Farther, Contrastive Learning Pulls Closer: A Two-Stage Approach to Mitigate Social Biases
As the representation capability of Pre-trained Language Models (PLMs) i...
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05/22/2023
DEGREE: Decomposition Based Explanation For Graph Neural Networks
Graph Neural Networks (GNNs) are gaining extensive attention for their a...
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03/21/2023
Black-box Backdoor Defense via Zero-shot Image Purification
Backdoor attacks inject poisoned data into the training set, resulting i...
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03/02/2023
Understanding and Unifying Fourteen Attribution Methods with Taylor Interactions
Various attribution methods have been developed to explain deep neural n...
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02/07/2023
Efficient XAI Techniques: A Taxonomic Survey
Recently, there has been a growing demand for the deployment of Explaina...
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08/25/2022
Shortcut Learning of Large Language Models in Natural Language Understanding: A Survey
Large language models (LLMs) have achieved state-of-the-art performance ...
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07/20/2022
Mitigating Algorithmic Bias with Limited Annotations
Existing work on fairness modeling commonly assumes that sensitive attri...
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06/29/2022
Fair Machine Learning in Healthcare: A Review
Benefiting from the digitization of healthcare data and the development ...
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06/17/2022
Accelerating Shapley Explanation via Contributive Cooperator Selection
Even though Shapley value provides an effective explanation for a DNN mo...
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10/16/2021
What do Compressed Large Language Models Forget? Robustness Challenges in Model Compression
Recent works have focused on compressing pre-trained language models (PL...
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06/23/2021
Fairness via Representation Neutralization
Existing bias mitigation methods for DNN models primarily work on learni...
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05/28/2021
A General Taylor Framework for Unifying and Revisiting Attribution Methods
Attribution methods provide an insight into the decision-making process ...
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05/17/2021
Learning Disentangled Representations for Time Series
Time-series representation learning is a fundamental task for time-serie...
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04/14/2021
Mutual Information Preserving Back-propagation: Learn to Invert for Faithful Attribution
Back propagation based visualizations have been proposed to interpret de...
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03/11/2021
Towards Interpreting and Mitigating Shortcut Learning Behavior of NLU models
Recent studies indicate that NLU models are prone to rely on shortcut fe...
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01/18/2021
Generative Counterfactuals for Neural Networks via Attribute-Informed Perturbation
With the wide use of deep neural networks (DNN), model interpretability ...
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11/17/2020
Deep Serial Number: Computational Watermarking for DNN Intellectual Property Protection
In this paper, we introduce DSN (Deep Serial Number), a new watermarking...
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08/21/2020
A Unified Taylor Framework for Revisiting Attribution Methods
Attribution methods have been developed to understand the decision makin...
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07/24/2020
Machine Learning Explanations to Prevent Overtrust in Fake News Detection
Combating fake news and misinformation propagation is a challenging task...
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06/15/2020
Mitigating Gender Bias in Captioning Systems
Image captioning has made substantial progress with huge supporting imag...
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06/15/2020
An Embarrassingly Simple Approach for Trojan Attack in Deep Neural Networks
With the widespread use of deep neural networks (DNNs) in high-stake app...
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04/23/2020
Adversarial Machine Learning: An Interpretation Perspective
Recent years have witnessed the significant advances of machine learning...
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10/03/2019
Score-CAM:Improved Visual Explanations Via Score-Weighted Class Activation Mapping
Recently, more and more attention has been drawn into the internal mecha...
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10/01/2019
Sub-Architecture Ensemble Pruning in Neural Architecture Search
Neural architecture search (NAS) is gaining more and more attention in r...
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09/13/2019
Towards Generalizable Forgery Detection with Locality-aware AutoEncoder
With advancements of deep learning techniques, it is now possible to gen...
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08/23/2019
Fairness in Deep Learning: A Computational Perspective
Deep learning is increasingly being used in high-stake decision making a...
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08/13/2019
Learning Credible Deep Neural Networks with Rationale Regularization
Recent explainability related studies have shown that state-of-the-art D...
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08/11/2019
SpecAE: Spectral AutoEncoder for Anomaly Detection in Attributed Networks
Anomaly detection aims to distinguish observations that are rare and dif...
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08/11/2019
Deep Structured Cross-Modal Anomaly Detection
Anomaly detection is a fundamental problem in data mining field with man...
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07/16/2019
Evaluating Explanation Without Ground Truth in Interpretable Machine Learning
Interpretable Machine Learning (IML) has become increasingly important i...
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07/08/2019
XFake: Explainable Fake News Detector with Visualizations
In this demo paper, we present the XFake system, an explainable fake new...
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03/27/2019
On Attribution of Recurrent Neural Network Predictions via Additive Decomposition
RNN models have achieved the state-of-the-art performance in a wide rang...
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07/31/2018
Techniques for Interpretable Machine Learning
Interpretable machine learning tackles the important problem that humans...
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03/19/2018