Image de couverture de livre

Locally-Minimal Probabilistic Explanations

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Izza, Yacine, Meel, Kuldeep S., Marques-Silva, Joao
Format:	Preprint
Publié:	2023
Sujets:	Machine Learning Artificial Intelligence
Accès en ligne:	Acceder al recurso
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

Delivering Inflated Explanations
par: Izza, Yacine, et autres
Publié: (2023)

Efficient Contrastive Explanations on Demand
par: Izza, Yacine, et autres
Publié: (2024)

Rigorous Explanations for Tree Ensembles
par: Izza, Yacine, et autres
Publié: (2026)

Most General Explanations of Tree Ensembles (Extended Version)
par: Izza, Yacine, et autres
Publié: (2025)

Distance-Restricted Explanations: Theoretical Underpinnings & Efficient Implementation
par: Izza, Yacine, et autres
Publié: (2024)

Probabilistic Explanations for Linear Models
par: Subercaseaux, Bernardo, et autres
Publié: (2024)

Quantifying Sensitivity for Tree Ensembles: A symbolic and compositional approach
par: Akshay, S., et autres
Publié: (2026)

On Trustworthy Rule-Based Models and Explanations
par: Siala, Mohamed, et autres
Publié: (2025)

Rigorous Probabilistic Guarantees for Robust Counterfactual Explanations
par: Marzari, Luca, et autres
Publié: (2024)

Counterfactual Explanations with Probabilistic Guarantees on their Robustness to Model Change
par: Stępka, Ignacy, et autres
Publié: (2024)

Multi-View Majority Vote Learning Algorithms: Direct Minimization of PAC-Bayesian Bounds
par: Hennequin, Mehdi, et autres
Publié: (2024)

Efficient Certified Reasoning for Binarized Neural Networks
par: Yang, Jiong, et autres
Publié: (2025)

Probabilistically Plausible Counterfactual Explanations with Normalizing Flows
par: Wielopolski, Patryk, et autres
Publié: (2024)

Trust Regions for Explanations via Black-Box Probabilistic Certification
par: Dhurandhar, Amit, et autres
Publié: (2024)

A Probabilistic Consensus-Driven Approach for Robust Counterfactual Explanations
par: Kostrzewa, Marcin, et autres
Publié: (2026)

FAME: Formal Abstract Minimal Explanation for Neural Networks
par: Boumazouza, Ryma, et autres
Publié: (2026)

Hyperparameter Optimisation with Practical Interpretability and Explanation Methods in Probabilistic Curriculum Learning
par: Salt, Llewyn, et autres
Publié: (2025)

Explainability is NOT a Game
par: Marques-Silva, Joao, et autres
Publié: (2023)

GNNInterpreter: A Probabilistic Generative Model-Level Explanation for Graph Neural Networks
par: Wang, Xiaoqi, et autres
Publié: (2022)

Learning to Solve the Constrained Most Probable Explanation Task in Probabilistic Graphical Models
par: Arya, Shivvrat, et autres
Publié: (2024)

Exploring Energy Landscapes for Minimal Counterfactual Explanations: Applications in Cybersecurity and Beyond
par: Evangelatos, Spyridon, et autres
Publié: (2025)

Engineering an Exact Pseudo-Boolean Model Counter
par: Yang, Suwei, et autres
Publié: (2023)

Uncovering Bugs in Formal Explainers: A Case Study with PyXAI
par: Huang, Xuanxiang, et autres
Publié: (2025)

Regional Explanations: Bridging Local and Global Variable Importance
par: Amoukou, Salim I., et autres
Publié: (2026)

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

GLEAMS: Bridging the Gap Between Local and Global Explanations
par: Visani, Giorgio, et autres
Publié: (2024)

Towards trustable SHAP scores
par: Letoffe, Olivier, et autres
Publié: (2024)

SHAP scores fail pervasively even when Lipschitz succeeds
par: Letoffe, Olivier, et autres
Publié: (2024)

Enhanced Rooftop Solar Panel Detection by Efficiently Aggregating Local Features
par: Kurte, Kuldeep, et autres
Publié: (2025)

BELLA: Black box model Explanations by Local Linear Approximations
par: Radulovic, Nedeljko, et autres
Publié: (2023)

Training and Evaluating Causal Forecasting Models for Time-Series
par: Crasson, Thomas, et autres
Publié: (2024)

SHAP-based Explanations are Sensitive to Feature Representation
par: Hwang, Hyunseung, et autres
Publié: (2025)

Exploring Local Explanations of Nonlinear Models Using Animated Linear Projections
par: Spyrison, Nicholas, et autres
Publié: (2022)

L2GTX: From Local to Global Time Series Explanations
par: Mekonnen, Ephrem Tibebe, et autres
Publié: (2026)

Model Counting in the Wild
par: Shaw, Arijit, et autres
Publié: (2024)

Towards Projected and Incremental Pseudo-Boolean Model Counting
par: Yang, Suwei, et autres
Publié: (2024)

An ASP-Based Framework for MUSes
par: Kabir, Mohimenul, et autres
Publié: (2025)

Approximate SMT Counting Beyond Discrete Domains
par: Shaw, Arijit, et autres
Publié: (2025)

Beyond Linear Surrogates: High-Fidelity Local Explanations for Black-Box Models
par: Shrestha, Sanjeev, et autres
Publié: (2025)

From SHAP Scores to Feature Importance Scores
par: Letoffe, Olivier, et autres
Publié: (2024)