NEURIPS 2025 "explainable ai" Papers
18 papers found
$\mathcal{X}^2$-DFD: A framework for e$\mathcal{X}$plainable and e$\mathcal{X}$tendable Deepfake Detection
Yize Chen, Zhiyuan Yan, Guangliang Cheng et al.
Advancing Interpretability of CLIP Representations with Concept Surrogate Model
Nhat Hoang-Xuan, Xiyuan Wei, Wanli Xing et al.
Contimask: Explaining Irregular Time Series via Perturbations in Continuous Time
Max Moebus, Björn Braun, Christian Holz
Disentangled Concepts Speak Louder Than Words: Explainable Video Action Recognition
Jongseo Lee, Wooil Lee, Gyeong-Moon Park et al.
Explainable Reinforcement Learning from Human Feedback to Improve Alignment
Shicheng Liu, Siyuan Xu, Wenjie Qiu et al.
Explainably Safe Reinforcement Learning
Sabine Rieder, Stefan Pranger, Debraj Chakraborty et al.
LeapFactual: Reliable Visual Counterfactual Explanation Using Conditional Flow Matching
Zhuo Cao, Xuan Zhao, Lena Krieger et al.
Minimizing False-Positive Attributions in Explanations of Non-Linear Models
Anders Gjølbye, Stefan Haufe, Lars Kai Hansen
Mol-LLaMA: Towards General Understanding of Molecules in Large Molecular Language Model
Dongki Kim, Wonbin Lee, Sung Ju Hwang
On Logic-based Self-Explainable Graph Neural Networks
Alessio Ragno, Marc Plantevit, Céline Robardet
Provable Gradient Editing of Deep Neural Networks
Zhe Tao, Aditya V Thakur
RadZero: Similarity-Based Cross-Attention for Explainable Vision-Language Alignment in Chest X-ray with Zero-Shot Multi-Task Capability
Jonggwon Park, Byungmu Yoon, Soobum Kim et al.
Regression-adjusted Monte Carlo Estimators for Shapley Values and Probabilistic Values
R. Teal Witter, Yurong Liu, Christopher Musco
Representational Difference Explanations
Neehar Kondapaneni, Oisin Mac Aodha, Pietro Perona
Scalable, Explainable and Provably Robust Anomaly Detection with One-Step Flow Matching
Zhong Li, Qi Huang, Yuxuan Zhu et al.
SHAP values via sparse Fourier representation
Ali Gorji, Andisheh Amrollahi, Andreas Krause
Smoothed Differentiation Efficiently Mitigates Shattered Gradients in Explanations
Adrian Hill, Neal McKee, Johannes Maeß et al.
Sound Logical Explanations for Mean Aggregation Graph Neural Networks
Matthew Morris, Ian Horrocks