Cover Image

Distributional Counterfactual Explanations With Optimal Transport

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	You, Lei, Cao, Lele, Nilsson, Mattias, Zhao, Bo, Lei, Lei
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

Joint Distribution-Informed Shapley Values for Sparse Counterfactual Explanations
by: You, Lei, et al.
Published: (2024)

DISCOVER: A Solver for Distributional Counterfactual Explanations
by: Gu, Yikai, et al.
Published: (2026)

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

Solving Prior Distribution Mismatch in Diffusion Models via Optimal Transport
by: Wang, Zhanpeng, et al.
Published: (2024)

Counterfactual Identifiability via Dynamic Optimal Transport
by: Ribeiro, Fabio De Sousa, et al.
Published: (2025)

LeapFactual: Reliable Visual Counterfactual Explanation Using Conditional Flow Matching
by: Cao, Zhuo, et al.
Published: (2025)

From Baselines to Transport Geodesics: Axiomatic Attribution via Optimal Generative Flows
by: Zhang, Cenwei, et al.
Published: (2026)

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)

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

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

Generating Counterfactual Explanations Using Cardinality Constraints
by: Ruiz-Torrubiano, Rubén
Published: (2024)

Counterfactual Explanations for Continuous Action Reinforcement Learning
by: Dong, Shuyang, et al.
Published: (2025)

On the Hardness of Computing Counterfactual and Semifactual Explanations in XAI
by: Artelt, André, et al.
Published: (2026)

On the Definition and Detection of Cherry-Picking in Counterfactual Explanations
by: Hinns, James, 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)

Flexible Counterfactual Explanations with Generative Models
by: Hellemans, Stig, et al.
Published: (2025)

Counterfactual Explanations for Deep Learning-Based Traffic Forecasting
by: Wang, Rushan, et al.
Published: (2024)

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

Game-theoretic Counterfactual Explanation for Graph Neural Networks
by: Chhablani, Chirag, et al.
Published: (2024)

Graph Edits for Counterfactual Explanations: A comparative study
by: Dimitriou, Angeliki, et al.
Published: (2024)

Counterfactual Explanations with Probabilistic Guarantees on their Robustness to Model Change
by: Stępka, Ignacy, et al.
Published: (2024)

FLEX: Feature Importance from Layered Counterfactual Explanations
by: Keshtmand, Nawid, et al.
Published: (2025)

Counterfactual Training: Teaching Models Plausible and Actionable Explanations
by: Altmeyer, Patrick, et al.
Published: (2026)

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

Explaining k-Nearest Neighbors: Abductive and Counterfactual Explanations
by: Barceló, Pablo, et al.
Published: (2025)

The Impact of Machine Learning Uncertainty on the Robustness of Counterfactual Explanations
by: Christodoulou, Leonidas, et al.
Published: (2026)

Multi-Agent Deep Reinforcement Learning for Distributed and Autonomous Platoon Coordination via Speed-regulation over Large-scale Transportation Networks
by: Wei, Dixiao, et al.
Published: (2024)

Probabilistically Plausible Counterfactual Explanations with Normalizing Flows
by: Wielopolski, Patryk, et al.
Published: (2024)

Enhancing Counterfactual Explanation Search with Diffusion Distance and Directional Coherence
by: Domnich, Marharyta, et al.
Published: (2024)

What-If Explanations Over Time: Counterfactuals for Time Series Classification
by: Schlegel, Udo, et al.
Published: (2026)

Provably Robust Bayesian Counterfactual Explanations under Model Changes
by: Duell, Jamie, et al.
Published: (2026)

A Probabilistic Consensus-Driven Approach for Robust Counterfactual Explanations
by: Kostrzewa, Marcin, et al.
Published: (2026)

Counterfactual Explanations for Hypergraph Neural Networks
by: Veglianti, Fabiano, et al.
Published: (2026)

Counterfactual Explanations for Clustering Models
by: Spagnol, Aurora, et al.
Published: (2024)

Adaptive Individual Uncertainty under Out-Of-Distribution Shift with Expert-Routed Conformal Prediction
by: Badkul, Amitesh, et al.
Published: (2025)

Optimal Transport for LLM Reward Modeling from Noisy Preference
by: Pan, Licheng, et al.
Published: (2026)

xai-cola: A Python library for sparsifying counterfactual explanations
by: Zhu, Lin, et al.
Published: (2026)

Joint Optimal Transport and Embedding for Network Alignment
by: Yu, Qi, et al.
Published: (2025)