Cover Image

Even-if Explanations: Formal Foundations, Priorities and Complexity

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Alfano, Gianvincenzo, Greco, Sergio, Mandaglio, Domenico, Parisi, Francesco, Shahbazian, Reza, Trubitsyna, Irina
Format:	Preprint
Published:	2024
Subjects:	Artificial Intelligence Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Counterfactual and Semifactual Explanations in Abstract Argumentation: Formal Foundations, Complexity and Computation
by: Alfano, Gianvincenzo, et al.
Published: (2024)

Cyclic Supports in Recursive Bipolar Argumentation Frameworks: Semantics and LP Mapping
by: Alfano, Gianvincenzo, et al.
Published: (2024)

Double Rectified Linear Unit-based Modular Semantics for Quantitative Bipolar Argumentation Framework
by: Alfano, Gianvincenzo, et al.
Published: (2026)

LLM-based Argument Mining meets Argumentation and Description Logics: a Unified Framework for Reasoning about Debates
by: Alfano, Gianvincenzo, et al.
Published: (2026)

A Total Variation Regularized Framework for Epilepsy-Related MRI Image Segmentation
by: Rabiee, Mehdi, et al.
Published: (2025)

Efficiently Computing Compact Formal Explanations
by: Wu, Min, et al.
Published: (2024)

A System for Comprehensive Assessment of RAG Frameworks
by: Rengo, Mattia, et al.
Published: (2025)

Towards Verified and Targeted Explanations through Formal Methods
by: Wang, Hanchen David, et al.
Published: (2026)

FAME: Formal Abstract Minimal Explanation for Neural Networks
by: Boumazouza, Ryma, et al.
Published: (2026)

Extending XReason: Formal Explanations for Adversarial Detection
by: Jemaa, Amira, et al.
Published: (2024)

Towards Piece-by-Piece Explanations for Chess Positions with SHAP
by: Spinnato, Francesco
Published: (2025)

Formal Explanations for Neuro-Symbolic AI
by: Paul, Sushmita, et al.
Published: (2024)

The GECo algorithm for Graph Neural Networks Explanation
by: Calderaro, Salvatore, et al.
Published: (2024)

Interval Abstractions for Robust Counterfactual Explanations
by: Jiang, Junqi, et al.
Published: (2024)

Rigorous Probabilistic Guarantees for Robust Counterfactual Explanations
by: Marzari, Luca, et al.
Published: (2024)

Robust Counterfactual Explanations in Machine Learning: A Survey
by: Jiang, Junqi, et al.
Published: (2024)

RobustX: Robust Counterfactual Explanations Made Easy
by: Jiang, Junqi, et al.
Published: (2025)

Tree of Thoughts as a Classical Heuristic Search Problem: Formal Foundations and Design Patterns
by: Sharon, Guni
Published: (2026)

Axiomatic Foundations of Counterfactual Explanations
by: Amgoud, Leila, et al.
Published: (2026)

Generalized Priority-Aware Shapley Value
by: Lee, Kiljae, et al.
Published: (2026)

Uncertainty Quantification as a Principled Foundation for Explainable Artificial Intelligence: A Case Study of Counterfactual Explanations
by: Sokol, Kacper, et al.
Published: (2025)

Provably Robust and Plausible Counterfactual Explanations for Neural Networks via Robust Optimisation
by: Jiang, Junqi, et al.
Published: (2023)

On Identifying Why and When Foundation Models Perform Well on Time-Series Forecasting Using Automated Explanations and Rating
by: Widener, Michael, et al.
Published: (2025)

Using Curiosity for an Even Representation of Tasks in Continual Offline Reinforcement Learning
by: Pathmanathan, Pankayaraj, et al.
Published: (2023)

Meta-Learning Objectives for Preference Optimization
by: Alfano, Carlo, et al.
Published: (2024)

Do Large Language Models Reason Causally Like Us? Even Better?
by: Dettki, Hanna M., et al.
Published: (2025)

Explanations are a Means to an End: Decision Theoretic Explanation Evaluation
by: Guo, Ziyang, et al.
Published: (2025)

Is Your Explanation Reliable: Confidence-Aware Explanation on Graph Neural Networks
by: Zhang, Jiaxing, et al.
Published: (2025)

ChronosAD: Leveraging Time Series Foundation Models for Accurate Anomaly Detection
by: Khan, Uzair, et al.
Published: (2026)

Goal-Oriented Time-Series Forecasting: Foundation Framework Design
by: Fechete, Luca-Andrei, et al.
Published: (2025)

Even Heads Fix Odd Errors: Mechanistic Discovery and Surgical Repair in Transformer Attention
by: Sandoval, Gustavo
Published: (2025)

Delivering Inflated Explanations
by: Izza, Yacine, et al.
Published: (2023)

Comparative Explanations: Explanation Guided Decision Making for Human-in-the-Loop Preference Selection
by: Chakraborty, Tanmay, et al.
Published: (2025)

Aligning Evaluation with Clinical Priorities: Calibration, Label Shift, and Error Costs
by: Flores, Gerardo A., et al.
Published: (2025)

FormalMATH: Benchmarking Formal Mathematical Reasoning of Large Language Models
by: Yu, Zhouliang, et al.
Published: (2025)

FEAT: A Linear-Complexity Foundation Model for Extremely Large Structured Data
by: Song, Zhenghang, et al.
Published: (2026)

Comparing Explanations is Not Enough, Explain the Change: New Standards are Needed to Explain Behavioral Shifts in Large Language Models
by: Ciaperoni, Martino, et al.
Published: (2026)

Mathematical Foundation of Interpretable Equivariant Surrogate Models
by: Colombini, Jacopo Joy, et al.
Published: (2025)

Symbolic Foundation Regressor on Complex Networks
by: Liu, Weiting, et al.
Published: (2025)

Sanity Checks for Explanation Uncertainty
by: Valdenegro-Toro, Matias, et al.
Published: (2024)