Feature Attribution from First Principles
Fuente:
arXiv
Saved in:
| Main Authors: | Taimeskhanov, Magamed, Garreau, Damien |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Towards Understanding Steering Strength
by: Taimeskhanov, Magamed, et al.
Published: (2026)
by: Taimeskhanov, Magamed, et al.
Published: (2026)
CAM-Based Methods Can See through Walls
by: Taimeskhanov, Magamed, et al.
Published: (2024)
by: Taimeskhanov, Magamed, et al.
Published: (2024)
Comparing Feature Importance and Rule Extraction for Interpretability on Text Data
by: Lopardo, Gianluigi, et al.
Published: (2022)
by: Lopardo, Gianluigi, et al.
Published: (2022)
On The Variability of Concept Activation Vectors
by: Wenkmann, Julia, et al.
Published: (2025)
by: Wenkmann, Julia, et al.
Published: (2025)
MMD-Flagger: Leveraging Maximum Mean Discrepancy to Detect Hallucinations
by: Mitsuzawa, Kensuke, et al.
Published: (2025)
by: Mitsuzawa, Kensuke, et al.
Published: (2025)
Faithful and Robust Local Interpretability for Textual Predictions
by: Lopardo, Gianluigi, et al.
Published: (2023)
by: Lopardo, Gianluigi, et al.
Published: (2023)
GLEAMS: Bridging the Gap Between Local and Global Explanations
by: Visani, Giorgio, et al.
Published: (2024)
by: Visani, Giorgio, et al.
Published: (2024)
Attention Meets Post-hoc Interpretability: A Mathematical Perspective
by: Lopardo, Gianluigi, et al.
Published: (2024)
by: Lopardo, Gianluigi, et al.
Published: (2024)
Understanding Post-hoc Explainers: The Case of Anchors
by: Lopardo, Gianluigi, et al.
Published: (2023)
by: Lopardo, Gianluigi, et al.
Published: (2023)
A Sea of Words: An In-Depth Analysis of Anchors for Text Data
by: Lopardo, Gianluigi, et al.
Published: (2022)
by: Lopardo, Gianluigi, et al.
Published: (2022)
The Risks of Recourse in Binary Classification
by: Fokkema, Hidde, et al.
Published: (2023)
by: Fokkema, Hidde, et al.
Published: (2023)
Are Ensembles Getting Better all the Time?
by: Mattei, Pierre-Alexandre, et al.
Published: (2023)
by: Mattei, Pierre-Alexandre, et al.
Published: (2023)
A High-Resolution Landscape Dataset for Concept-Based XAI With Application to Species Distribution Models
by: de la Brosse, Augustin, et al.
Published: (2026)
by: de la Brosse, Augustin, et al.
Published: (2026)
Explainability as statistical inference
by: Senetaire, Hugo Henri Joseph, et al.
Published: (2022)
by: Senetaire, Hugo Henri Joseph, et al.
Published: (2022)
The Attribution Contract: Feature Attribution for Generative Language Models
by: Nguyen, Giang
Published: (2026)
by: Nguyen, Giang
Published: (2026)
AttributionLab: Faithfulness of Feature Attribution Under Controllable Environments
by: Zhang, Yang, et al.
Published: (2023)
by: Zhang, Yang, et al.
Published: (2023)
Disentangling Interactions and Dependencies in Feature Attribution
by: König, Gunnar, et al.
Published: (2024)
by: König, Gunnar, et al.
Published: (2024)
Model Generalization on Text Attribute Graphs: Principles with Large Language Models
by: Wang, Haoyu, et al.
Published: (2025)
by: Wang, Haoyu, et al.
Published: (2025)
Explaining Concept Shift with Interpretable Feature Attribution
by: Lyu, Ruiqi, et al.
Published: (2025)
by: Lyu, Ruiqi, et al.
Published: (2025)
Model Guidance via Robust Feature Attribution
by: Ghitu, Mihnea, et al.
Published: (2025)
by: Ghitu, Mihnea, et al.
Published: (2025)
Boundary-Aware Uncertainty for Feature Attribution Explainers
by: Hill, Davin, et al.
Published: (2022)
by: Hill, Davin, et al.
Published: (2022)
Missingness Bias Calibration in Feature Attribution Explanations
by: Sridhar, Shailesh, et al.
Published: (2026)
by: Sridhar, Shailesh, et al.
Published: (2026)
Impossibility Theorems for Feature Attribution
by: Bilodeau, Blair, et al.
Published: (2022)
by: Bilodeau, Blair, et al.
Published: (2022)
The Hidden Influence of Latent Feature Magnitude When Learning with Imbalanced Data
by: Dablain, Damien A., et al.
Published: (2024)
by: Dablain, Damien A., et al.
Published: (2024)
Probabilistic Stability Guarantees for Feature Attributions
by: Jin, Helen, et al.
Published: (2025)
by: Jin, Helen, et al.
Published: (2025)
TinyTorch: Building Machine Learning Systems from First Principles
by: Reddi, Vijay Janapa
Published: (2026)
by: Reddi, Vijay Janapa
Published: (2026)
Enhancing Visual Feature Attribution via Weighted Integrated Gradients
by: Tuan, Kien Tran Duc, et al.
Published: (2025)
by: Tuan, Kien Tran Duc, et al.
Published: (2025)
Consistency of Feature Attribution in Deep Learning Architectures for Multi-Omics
by: Claborne, Daniel, et al.
Published: (2025)
by: Claborne, Daniel, et al.
Published: (2025)
Toward Understanding the Disagreement Problem in Neural Network Feature Attribution
by: Koenen, Niklas, et al.
Published: (2024)
by: Koenen, Niklas, et al.
Published: (2024)
Exploring the Relationship Between Feature Attribution Methods and Model Performance
by: Silva, Priscylla, et al.
Published: (2024)
by: Silva, Priscylla, et al.
Published: (2024)
Partial Order in Chaos: Consensus on Feature Attributions in the Rashomon Set
by: Laberge, Gabriel, et al.
Published: (2021)
by: Laberge, Gabriel, et al.
Published: (2021)
Hebbian Learning from First Principles
by: Albanese, Linda, et al.
Published: (2024)
by: Albanese, Linda, et al.
Published: (2024)
MiniGPT: Rebuilding GPT from First Principles
by: Joseph, Jibin
Published: (2026)
by: Joseph, Jibin
Published: (2026)
On the Properties of Feature Attribution for Supervised Contrastive Learning
by: Arrighi, Leonardo, et al.
Published: (2026)
by: Arrighi, Leonardo, et al.
Published: (2026)
Model Monitoring in the Absence of Labeled Data via Feature Attributions Distributions
by: Mougan, Carlos
Published: (2025)
by: Mougan, Carlos
Published: (2025)
GRAFT: Auditing Graph Neural Networks via Global Feature Attribution
by: Sahoo, Rishi Raj, et al.
Published: (2026)
by: Sahoo, Rishi Raj, et al.
Published: (2026)
Stochastic Amortization: A Unified Approach to Accelerate Feature and Data Attribution
by: Covert, Ian, et al.
Published: (2024)
by: Covert, Ian, et al.
Published: (2024)
Energy-Based Model for Accurate Estimation of Shapley Values in Feature Attribution
by: Lu, Cheng, et al.
Published: (2024)
by: Lu, Cheng, et al.
Published: (2024)
Training Feature Attribution for Vision Models
by: Bacha, Aziz, et al.
Published: (2025)
by: Bacha, Aziz, et al.
Published: (2025)
Correlation-Aware Feature Attribution Based Explainable AI
by: Sengupta, Poushali, et al.
Published: (2025)
by: Sengupta, Poushali, et al.
Published: (2025)
Similar Items
-
Towards Understanding Steering Strength
by: Taimeskhanov, Magamed, et al.
Published: (2026) -
CAM-Based Methods Can See through Walls
by: Taimeskhanov, Magamed, et al.
Published: (2024) -
Comparing Feature Importance and Rule Extraction for Interpretability on Text Data
by: Lopardo, Gianluigi, et al.
Published: (2022) -
On The Variability of Concept Activation Vectors
by: Wenkmann, Julia, et al.
Published: (2025) -
MMD-Flagger: Leveraging Maximum Mean Discrepancy to Detect Hallucinations
by: Mitsuzawa, Kensuke, et al.
Published: (2025)