Perturbation-Based Feature Attribution

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5 papers in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

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Latest papers 17

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Towards Explainable Benchmarking for Data-driven Post-Wildfire Debris Flow Prediction
Oct 5, 2026Zhisheng Qi, Li Zhu, Utkarsh Sahu +3Perturbation-Based Feature Attribution Forecasting Benchmarks
University of Georgia Athens, Georgia, USA · University of Oregon Eugene, Oregon, USA
A Comparative Explainability Framework for DeBERTa-v3 in Zero-Shot Medical Abstract Classification
Oct 1, 2026Javier Diaz Esteban-Herreros, David Muñoz-Valero, Raquel Martínez-España +2Transformer Interpretability Perturbation-Based Feature Attribution
Department of Technologies and Information Systems, Universidad de Castilla-La Mancha, Avenida Carlos III, s/n, Toledo, 45071, Spain · University of Murcia, Avenida Teniente Flomesta, 5, Murcia, 3003, Spain · Murcian Bio-Health Institute (IMIB-Arrixaca), Pabellón Docente del Hospital Clínico Universitario Virgen de la Arrixaca, Murcia, 3120, Spain
When Is an SAE Feature Interpretable? A Validation Ladder for EEG Foundation Models
Sep 28, 2026Yucong Cao, Chenqi Li, Tingting ZhuPerturbation-Based Feature Attribution Sparse Autoencoders
Department of Engineering Science University of Oxford
Mask-Induced Displacement in Audio XAI via Logit Trajectory Decomposition
Sep 27, 2026Nico García-Peguinho, David Kelly, Fabrizio Smeraldi +1Explainable Artificial Intelligence Perturbation-Based Feature Attribution
School of Electronic Engineering and Computer Science, Queen Mary University of London · Department of Informatics, King's College London · Department of Informatics, King’s College London
Faithful Faithfulness Evaluations: Challenges & Pitfalls Learned from a Breast MRI Case Study
Sep 22, 2026Peachapong Poolpol, Henrik H. J. Detjen, Eike PetersenPerturbation-Based Feature Attribution Saliency Map Evaluation
Fraunhofer Institute for Digital Medicine MEVIS, Bremen, Germany · Deggendorf Institute of Technology, Deggendorf, Germany
EEG-Xplain: Decoding Neural Black-Boxes of EEG Foundation Models
Sep 14, 2026Hansong Ma, Junxiao WangGradient-Based Attribution Perturbation-Based Feature Attribution
Guangzhou University, China
Decomposition of Evidence, Contradiction, and Fragility in Perturbation Responses
Aug 13, 2026Lei YouFeature Attribution Perturbation-Based Feature Attribution
Technical University of Denmark
On the Disagreement in Perturbation-based xAI -- Benchmarking Perturbation Choices for Flood Detection from SAR Images
Jul 16, 2026Anastasia Schlegel, Ronny HänschFeature Attribution Perturbation-Based Feature Attribution
Microwaves and Radar Institute, German Aerospace Center (DLR), Weßling, 82234, Germany · Chair of Remote Sensing Technology, Department of Aerospace and Geodesy, Technical University of Munich, Munich, 80333, Germany
Local Additive Feature Attribution: A Mathematical Taxonomy and Reporting Checklist
Jul 15, 2026Rebecca Afriyie Sarpong, Daniel CommeyGradient-Based Attribution Feature Attribution
A Good Initialization is All You Need for Faithful Visual Attribution
Jul 7, 2026Zihan Gu, Jiayu Wang, Hua Zhang +1Feature Attribution Perturbation-Based Feature Attribution
Institute of Information Engineering, Chinese Academy of Sciences · School of Cyber Security, University of Chinese Academy of Sciences
Re-Ranking Through an Attribution Lens for Citation Quality in Legal QA
Jun 2, 2026Mohamed Hesham Elganayni, Selim SalehCross-Encoder Reranking Feature Attribution
Technical University of Munich, Munich, Germany
Learning Quantifiable Visual Explanations Without Ground-Truth
May 18, 2026Amritpal Singh, Andrey Barsky, Mohamed Ali Souibgui +2Perturbation-Based Feature Attribution Causal Interpretability
Computer Vision Center, Barcelona, Spain · Autonomous University of Barcelona, Spain
From Weight Perturbation to Feature Attribution for Explaining Fully Connected Neural Networks
May 14, 2026Thodoris Lymperopoulos, Denia KanellopoulouFeature Attribution Perturbation-Based Feature Attribution
NCSR Demokritos Athens, Greece
FAME: Feature Activation Map Explanation on Image Classification and Face Recognition
May 12, 2026Xinyi Zhang, Manuel GüntherGradient-Based Attribution Feature Attribution
Department of Informatics, University of Zurich
BAMI: Training-Free Bias Mitigation in GUI Grounding
May 7, 2026Borui Zhang, Bo Zhang, Bo Wang +6Perturbation-Based Feature Attribution GUI Grounding
Tsinghua University, China · Lenovo Research, China
One Permutation Is All You Need: Fast, Deterministic Feature Importance and Model Stress-Testing
Dec 15, 2025Albert DoradorPerturbation-Based Feature Attribution Algorithmic Auditing
Explainable embeddings with Distance Explainer
May 21, 2025Christiaan Meijer, E. G. Patrick BosFeature Attribution Perturbation-Based Feature Attribution
Netherlands eScience Center, Science Park 402, 1098 XH Amsterdam, Netherlands · Center for Information Technology, University of Groningen, P.O. Box 11044, 9700 CA Groningen, Netherlands

Oct 5, 2026, cs.LGJ/K move · Enter open · S save

Towards Explainable Benchmarking for Data-driven Post-Wildfire Debris Flow Prediction

Zhisheng Qi, Li Zhu, Utkarsh Sahu, Douglas Tommey+2

University of Georgia Athens, Georgia, USA · University of Oregon Eugene, Oregon, USA

Perturbation-Based Feature Attribution Forecasting Benchmarks Spatiotemporal Forecasting

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Post-wildfire debris flows (PFDFs) are destructive sediment-laden hazards triggered when intense rainfall strikes recently burned terrain, destabilizing hillslopes and threatening infrastructure, local economies, and community safety. Data-driven methods have been proposed to learn predictive patterns directly from historical PFDF observations. However, the current research landscape of data-driven PFDF prediction remains highly fragmented across feature spaces, model architectures, and evaluation protocols, making rigorous comparison and the derivation of scientific insights difficult. Moreover, existing studies lack a systematic investigation into the relative importance of heterogeneous factors (e.g., meteorological conditions, terrain characteristics, soil properties, and burn severity) in triggering PFDF. To address these limitations, we present a unified benchmark for data-driven PFDF prediction, enabling fair and comprehensive evaluation across diverse models and feature configurations. Furthermore, to better understand the underlying drivers of PFDF formation, we propose a reinforcement learning-based feature selection framework that identifies factors whose perturbations render positive and negative events indistinguishable, thereby discovering the regional underlying mechanisms of PFDF occurrence across regions. Our code and benchmark are publicly available at https://github.com/KINDLab-Fly/PFDF-Benchmark.

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