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← Core Methods
Machine Learning
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Core Methods
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Interpretability
349 directly classified papers
Papers per year
2008: 1
2014: 1
2015: 2
2016: 4
2017: 4
2018: 10
2019: 29
2020: 41
2021: 40
2022: 65
2023: 55
2024: 56
2025: 41
Papers
ProtoLens: Advancing Prototype Learning for Fine-Grained Interpretability in Text Classification
ACL 2025
IRT-Router: Effective and Interpretable Multi-LLM Routing via Item Response Theory
ACL 2025
Faithful and Accurate Self-Attention Attribution for Message Passing Neural Networks via the Computation Tree Viewpoint
AAAI 2025
No Questions are Stupid, but some are Poorly Posed: Understanding Poorly-Posed Information-Seeking Questions
ACL 2025
Neural Reasoning for Sure Through Constructing Explainable Models
AAAI 2025
Accurate Estimation of Feature Importance Faithfulness for Tree Models
AAAI 2025
Towards Trustable SHAP Scores
AAAI 2025
A Robust Prototype-Based Network with Interpretable RBF Classifier Foundations
AAAI 2025
Probabilistic Explanations for Linear Models
AAAI 2025
Interpretable Failure Detection with Human-Level Concepts
AAAI 2025
AutoSciLab: A Self-Driving Laboratory for Interpretable Scientific Discovery
AAAI 2025
BEE: Metric-Adapted Explanations via Baseline Exploration-Exploitation
AAAI 2025
Even-if Explanations: Formal Foundations, Priorities and Complexity
AAAI 2025
Disentangling, Amplifying, and Debiasing: Learning Disentangled Representations for Fair Graph Neural Networks
AAAI 2025
Prompt-CAM: Making Vision Transformers Interpretable for Fine-Grained Analysis
CVPR 2025
Explainable Neural Networks with Guarantee: A Sparse Estimation Approach
AAAI 2025
Unlocking Better Closed-Set Alignment Based on Neural Collapse for Open-Set Recognition
AAAI 2025
ReX: A Framework for Incorporating Temporal Information in Model-Agnostic Local Explanation Techniques
AAAI 2025
Higher Order Structures for Graph Explanations
AAAI 2025
Functional Connectomes of Neural Networks
AAAI 2025
InteDisUX: Intepretation-Guided Discriminative User-Centric Explanation for Time Series
AAAI 2025
Beyond Accuracy: On the Effects of Fine-Tuning Towards Vision-Language Model’s Prediction Rationality
AAAI 2025
ProtoArgNet: Interpretable Image Classification with Super-Prototypes and Argumentation
AAAI 2025
Interpretable Image Classification via Non-parametric Part Prototype Learning
CVPR 2025
Using Shapley interactions to understand how models use structure
ACL 2025
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