Cover Image

Delivering Inflated Explanations

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Izza, Yacine, Ignatiev, Alexey, Stuckey, Peter, Marques-Silva, Joao
Format:	Preprint
Published:	2023
Subjects:	Artificial Intelligence Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Rigorous Explanations for Tree Ensembles
by: Izza, Yacine, et al.
Published: (2026)

Most General Explanations of Tree Ensembles (Extended Version)
by: Izza, Yacine, et al.
Published: (2025)

Locally-Minimal Probabilistic Explanations
by: Izza, Yacine, et al.
Published: (2023)

Distance-Restricted Explanations: Theoretical Underpinnings & Efficient Implementation
by: Izza, Yacine, et al.
Published: (2024)

Efficient Contrastive Explanations on Demand
by: Izza, Yacine, et al.
Published: (2024)

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

Efficient & Correct Predictive Equivalence for Decision Trees
by: Marques-Silva, Joao, et al.
Published: (2025)

Uncovering Bugs in Formal Explainers: A Case Study with PyXAI
by: Huang, Xuanxiang, et al.
Published: (2025)

A Formal Framework for the Explanation of Finite Automata Decisions
by: Granada, Jaime Cuartas, et al.
Published: (2026)

On Trustworthy Rule-Based Models and Explanations
by: Siala, Mohamed, et al.
Published: (2025)

Explainability is NOT a Game
by: Marques-Silva, Joao, et al.
Published: (2023)

From Noise to Precision: A Diffusion-Driven Approach to Zero-Inflated Precipitation Prediction
by: Gao, Wentao, et al.
Published: (2025)

Towards trustable SHAP scores
by: Letoffe, Olivier, et al.
Published: (2024)

SHAP scores fail pervasively even when Lipschitz succeeds
by: Letoffe, Olivier, et al.
Published: (2024)

Training and Evaluating Causal Forecasting Models for Time-Series
by: Crasson, Thomas, et al.
Published: (2024)

SHAP-based Explanations are Sensitive to Feature Representation
by: Hwang, Hyunseung, et al.
Published: (2025)

LIMEtree: Consistent and Faithful Surrogate Explanations of Multiple Classes
by: Sokol, Kacper, et al.
Published: (2020)

From SHAP Scores to Feature Importance Scores
by: Letoffe, Olivier, et al.
Published: (2024)

The Cost of Relaxation: Evaluating the Error in Convex Neural Network Verification
by: Papamichail, Merkouris, et al.
Published: (2026)

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)

DeliverAI: Reinforcement Learning Based Distributed Path-Sharing Network for Food Deliveries
by: Mehra, Ashman, et al.
Published: (2023)

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

Self-organizing maps for water quality assessment in reservoirs and lakes: A systematic literature review
by: Almegdadi, Oraib, et al.
Published: (2025)

Multi-View Majority Vote Learning Algorithms: Direct Minimization of PAC-Bayesian Bounds
by: Hennequin, Mehdi, et al.
Published: (2024)

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

Suboptimal Shapley Value Explanations
by: Lu, Xiaolei
Published: (2025)

How Explanations Leak the Decision Logic: Stealing Graph Neural Networks via Explanation Alignment
by: Ma, Bin, et al.
Published: (2025)

Calibrated Explanations: with Uncertainty Information and Counterfactuals
by: Lofstrom, Helena, et al.
Published: (2023)

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

Explanation Multiplicity in SHAP: Characterization and Assessment
by: Hwang, Hyunseung, et al.
Published: (2026)

Distributional Counterfactual Explanations With Optimal Transport
by: You, Lei, et al.
Published: (2024)

Galaxy Morphology Classification with Counterfactual Explanation
by: Cao, Zhuo, et al.
Published: (2025)

Provable Robust Saliency-based Explanations
by: Chen, Chao, et al.
Published: (2022)

Reflective-Net: Learning from Explanations
by: Schneider, Johannes, et al.
Published: (2020)

The Effect of Data Poisoning on Counterfactual Explanations
by: Artelt, André, et al.
Published: (2024)

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

Credible CO2 Comparisons: A Machine Learning Approach to Vehicle Powertrain Assessment
by: David, Rodrigo Pereira, et al.
Published: (2026)

Deep Backtracking Counterfactuals for Causally Compliant Explanations
by: Kladny, Klaus-Rudolf, et al.
Published: (2023)

Robust Stochastic Graph Generator for Counterfactual Explanations
by: Prado-Romero, Mario Alfonso, et al.
Published: (2023)