Image de couverture de livre

Explanation Beyond Intuition: A Testable Criterion for Inherent Explainability

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Merry, Michael, Riddle, Pat, Warren, Jim
Format:	Preprint
Publié:	2025
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

PropNEAT -- Efficient GPU-Compatible Backpropagation over NeuroEvolutionary Augmenting Topology Networks
par: Merry, Michael, et autres
Publié: (2024)

A Testable Certificate for Constant Collapse in Teacher-Guided VAEs
par: Zhang, Zegu, et autres
Publié: (2026)

ICPRL: Acquiring Physical Intuition from Interactive Control
par: Xu, Xinrun, et autres
Publié: (2026)

Explainable AI in Time-Sensitive Scenarios: Prefetched Offline Explanation Model
par: Russo, Fabio Michele, et autres
Publié: (2025)

Transformer Is Inherently a Causal Learner
par: Wang, Xinyue, et autres
Publié: (2026)

From Predictions to Explanations: Explainable AI for Autism Diagnosis and Identification of Critical Brain Regions
par: Gupta, Kush, et autres
Publié: (2025)

Uncertainty Quantification as a Principled Foundation for Explainable Artificial Intelligence: A Case Study of Counterfactual Explanations
par: Sokol, Kacper, et autres
Publié: (2025)

Human in the Latent Loop (HILL): Interactively Guiding Model Training Through Human Intuition
par: Geissler, Daniel, et autres
Publié: (2025)

On the Emergence of Thinking in LLMs I: Searching for the Right Intuition
par: Ye, Guanghao, et autres
Publié: (2025)

Testability of Instrumental Variables in Additive Nonlinear, Non-Constant Effects Models
par: Guo, Xichen, et autres
Publié: (2024)

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

Beyond Models! Explainable Data Valuation and Metric Adaption for Recommendation
par: Jia, Renqi, et autres
Publié: (2025)

Intuition to Evidence: Measuring AI's True Impact on Developer Productivity
par: Kumar, Anand, et autres
Publié: (2025)

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

Natural Language Requirements Testability Measurement Based on Requirement Smells
par: Zakeri-Nasrabadi, Morteza, et autres
Publié: (2024)

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

Beyond One-Size-Fits-All: Adapting Counterfactual Explanations to User Objectives
par: Mastromichalakis, Orfeas Menis, et autres
Publié: (2024)

Beyond Additivity: Sparse Isotonic Shapley Regression toward Nonlinear Explainability
par: She, Jialai
Publié: (2025)

T-REX: Mixture-of-Rank-One-Experts with Semantic-aware Intuition for Multi-task Large Language Model Finetuning
par: Zhang, Rongyu, et autres
Publié: (2024)

On the Laplace Approximation as Model Selection Criterion for Gaussian Processes
par: Besginow, Andreas, et autres
Publié: (2024)

Dual-Criterion Curriculum Learning: Application to Temporal Data
par: Abel, Gaspard, et autres
Publié: (2026)

Inherently Interpretable Time Series Classification via Multiple Instance Learning
par: Early, Joseph, et autres
Publié: (2023)

From Explainability to Interpretability: Interpretable Policies in Reinforcement Learning Via Model Explanation
par: Li, Peilang, et autres
Publié: (2025)

Spectral Clustering for Crowdsourcing with Inherently Distinct Task Types
par: Mandal, Saptarshi, et autres
Publié: (2023)

Predictive Modeling and Explainable AI for Veterinary Safety Profiles, Residue Assessment, and Health Outcomes Using Real-World Data and Physicochemical Properties
par: Sholehrasa, Hossein, et autres
Publié: (2025)

The Role of Inherent Bellman Error in Offline Reinforcement Learning with Linear Function Approximation
par: Golowich, Noah, et autres
Publié: (2024)

Fixed Point Explainability
par: La Malfa, Emanuele, et autres
Publié: (2025)

Multi-Objective Reinforcement Learning with Max-Min Criterion: A Game-Theoretic Approach
par: Byeon, Woohyeon, et autres
Publié: (2025)

Quotient Normalized Maximum Likelihood Criterion for Learning Bayesian Network Structures
par: Silander, Tomi, et autres
Publié: (2024)

Clustered FedStack: Intermediate Global Models with Bayesian Information Criterion
par: Shaik, Thanveer, et autres
Publié: (2023)

VCformer: Variable Correlation Transformer with Inherent Lagged Correlation for Multivariate Time Series Forecasting
par: Yang, Yingnan, et autres
Publié: (2024)

Using Artificial Intuition in Distinct, Minimalist Classification of Scientific Abstracts for Management of Technology Portfolios
par: Ranka, Prateek, et autres
Publié: (2025)

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

Explanations are a Means to an End: Decision Theoretic Explanation Evaluation
par: Guo, Ziyang, et autres
Publié: (2025)

Is Your Explanation Reliable: Confidence-Aware Explanation on Graph Neural Networks
par: Zhang, Jiaxing, et autres
Publié: (2025)

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

Comparative Explanations: Explanation Guided Decision Making for Human-in-the-Loop Preference Selection
par: Chakraborty, Tanmay, et autres
Publié: (2025)

An Information-Theoretic Criterion for Efficient Data Synthesis
par: Li, Hanyu, et autres
Publié: (2026)

An Information Criterion for Controlled Disentanglement of Multimodal Data
par: Wang, Chenyu, et autres
Publié: (2024)

Context-Aware Visualization for Explainable AI Recommendations in Social Media: A Vision for User-Aligned Explanations
par: Alkhateeb, Banan, et autres
Publié: (2025)