Image de couverture de livre

Model Explanations via the Axiomatic Causal Lens

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Biradar, Gagan, Viswanathan, Vignesh, Zick, Yair
Format:	Preprint
Publié:	2021
Sujets:	Machine Learning
Accès en ligne:	Acceder al recurso
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

Simple Steps to Success: A Method for Step-Based Counterfactual Explanations
par: Hamer, Jenny, et autres
Publié: (2023)

Stable Matching under Matroid Rank Valuations
par: Eden, Alon, et autres
Publié: (2025)

On the Hardness of Fair Allocation under Ternary Valuations
par: Fitzsimmons, Zack, et autres
Publié: (2024)

An Axiomatic Approach to Model-Agnostic Concept Explanations
par: Feng, Zhili, et autres
Publié: (2024)

RankSHAP: Shapley Value Based Feature Attributions for Learning to Rank
par: Chowdhury, Tanya, et autres
Publié: (2024)

Hedonic Neurons: A Mechanistic Mapping of Latent Coalitions in Transformer MLPs
par: Chowdhury, Tanya, et autres
Publié: (2025)

Axiomatic Foundations of Counterfactual Explanations
par: Amgoud, Leila, et autres
Publié: (2026)

Fair and Welfare-Efficient Constrained Multi-matchings under Uncertainty
par: Lobo, Elita, et autres
Publié: (2024)

Percentile Criterion Optimization in Offline Reinforcement Learning
par: Lobo, Elita A., et autres
Publié: (2024)

Graph Neural Network Causal Explanation via Neural Causal Models
par: Behnam, Arman, et autres
Publié: (2024)

HistoLens: An Interactive XAI Toolkit for Verifying and Mitigating Flaws in Vision-Language Models for Histopathology
par: Vissapragada, Sandeep, et autres
Publié: (2025)

Teaching Transformers Causal Reasoning through Axiomatic Training
par: Vashishtha, Aniket, et autres
Publié: (2024)

Axiomatic Explainer Globalness via Optimal Transport
par: Hill, Davin, et autres
Publié: (2024)

Identifiable Latent Polynomial Causal Models Through the Lens of Change
par: Liu, Yuhang, et autres
Publié: (2023)

Linking Model Intervention to Causal Interpretation in Model Explanation
par: Cheng, Debo, et autres
Publié: (2024)

Heterogeneous Multi-Agent Bandits with Parsimonious Hints
par: Mirfakhar, Amirmahdi, et autres
Publié: (2025)

Attention Augmented GNN RNN-Attention Models for Advanced Cybersecurity Intrusion Detection
par: Biradar, Jayant, et autres
Publié: (2025)

Attention Deficits in Language Models: Causal Explanations for Procedural Hallucinations
par: Karim, Ahmed, et autres
Publié: (2026)

Quantized Convolutional Neural Networks Through the Lens of Partial Differential Equations
par: Ben-Yair, Ido, et autres
Publié: (2021)

Causality-Aware Local Interpretable Model-Agnostic Explanations
par: Cinquini, Martina, et autres
Publié: (2022)

Towards AI Transparency and Accountability: A Global Framework for Exchanging Information on AI Systems
par: Buckley, Warren, et autres
Publié: (2023)

Causal Graph Profiling via Structural Divergence for Robust Anomaly Detection in Cyber-Physical Systems
par: Malarkkan, Arun Vignesh, et autres
Publié: (2025)

Wasserstein Distributionally Robust Optimization Through the Lens of Structural Causal Models and Individual Fairness
par: Ehyaei, Ahmad-Reza, et autres
Publié: (2025)

Validating Mechanistic Interpretations: An Axiomatic Approach
par: Palumbo, Nils, et autres
Publié: (2024)

A Model of Causal Explanation on Neural Networks for Tabular Data
par: Isozaki, Takashi, et autres
Publié: (2025)

Towards Understanding Extrapolation: a Causal Lens
par: Kong, Lingjing, et autres
Publié: (2025)

Explaining Risks: Axiomatic Risk Attributions for Financial Models
par: Chen, Dangxing
Publié: (2025)

Feature Attribution with Necessity and Sufficiency via Dual-stage Perturbation Test for Causal Explanation
par: Chen, Xuexin, et autres
Publié: (2024)

An Axiomatic Definition of Hierarchical Clustering
par: Arias-Castro, Ery, et autres
Publié: (2024)

Practical do-Shapley Explanations with Estimand-Agnostic Causal Inference
par: Parafita, Álvaro, et autres
Publié: (2025)

Causal Explanation of Concept Drift -- A Truly Actionable Approach
par: Komnick, David, et autres
Publié: (2025)

New User Event Prediction Through the Lens of Causal Inference
par: Yuchi, Henry Shaowu, et autres
Publié: (2024)

Deep Backtracking Counterfactuals for Causally Compliant Explanations
par: Kladny, Klaus-Rudolf, et autres
Publié: (2023)

An Axiomatic Approach to Loss Aggregation and an Adapted Aggregating Algorithm
par: Pacheco, Armando J. Cabrera, et autres
Publié: (2024)

Axiomatization of Gradient Smoothing in Neural Networks
par: Zhou, Linjiang, et autres
Publié: (2024)

Counterfactual Explanations of Black-box Machine Learning Models using Causal Discovery with Applications to Credit Rating
par: Takahashi, Daisuke, et autres
Publié: (2024)

Causally-Guided Automated Feature Engineering with Multi-Agent Reinforcement Learning
par: Malarkkan, Arun Vignesh, et autres
Publié: (2026)

Rethinking Bivariate Causal Discovery Through the Lens of Exchangeability
par: Brogueira, Tiago, et autres
Publié: (2025)

A Causal Lens for Learning Long-term Fair Policies
par: Lear, Jacob, et autres
Publié: (2025)

Towards Complete Causal Explanation with Expert Knowledge
par: Venkateswaran, Aparajithan, et autres
Publié: (2024)