Saved in:
| Main Authors: | Harvey, Joshua S., Feng, Guanchao, Meesala, Sai Anusha, Zhao, Tina, Mehta, Dhagash |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2510.27397 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Open Set Recognition for Random Forest
by: Feng, Guanchao, et al.
Published: (2024)
by: Feng, Guanchao, et al.
Published: (2024)
Explainable Unsupervised Anomaly Detection with Random Forest
by: Harvey, Joshua S., et al.
Published: (2025)
by: Harvey, Joshua S., et al.
Published: (2025)
Quantile Regression using Random Forest Proximities
by: Li, Mingshu, et al.
Published: (2024)
by: Li, Mingshu, et al.
Published: (2024)
Enhanced Local Explainability and Trust Scores with Random Forest Proximities
by: Rosaler, Joshua, et al.
Published: (2023)
by: Rosaler, Joshua, et al.
Published: (2023)
Case-based Explainability for Random Forest: Prototypes, Critics, Counter-factuals and Semi-factuals
by: Yampolsky, Gregory, et al.
Published: (2024)
by: Yampolsky, Gregory, et al.
Published: (2024)
SCI: A Metacognitive Control for Signal Dynamics
by: Meesala, Vishal Joshua
Published: (2025)
by: Meesala, Vishal Joshua
Published: (2025)
S-CFE: Simple Counterfactual Explanations
by: Sadiku, Shpresim, et al.
Published: (2024)
by: Sadiku, Shpresim, et al.
Published: (2024)
AI versus AI in Financial Crimes and Detection: GenAI Crime Waves to Co-Evolutionary AI
by: Kurshan, Eren, et al.
Published: (2024)
by: Kurshan, Eren, et al.
Published: (2024)
TACE: Tumor-Aware Counterfactual Explanations
by: Rossi, Eleonora Beatrice, et al.
Published: (2024)
by: Rossi, Eleonora Beatrice, et al.
Published: (2024)
CONFEX: Uncertainty-Aware Counterfactual Explanations with Conformal Guarantees
by: Bilkhoo, Aman, et al.
Published: (2025)
by: Bilkhoo, Aman, et al.
Published: (2025)
Can an unsupervised clustering algorithm reproduce a categorization system?
by: Castellanos, Nathalia, et al.
Published: (2024)
by: Castellanos, Nathalia, et al.
Published: (2024)
Counterfactual Explanations for Continuous Action Reinforcement Learning
by: Dong, Shuyang, et al.
Published: (2025)
by: Dong, Shuyang, et al.
Published: (2025)
DISCOVER: A Solver for Distributional Counterfactual Explanations
by: Gu, Yikai, et al.
Published: (2026)
by: Gu, Yikai, et al.
Published: (2026)
Interpretable Network-assisted Random Forest+
by: Tang, Tiffany M., et al.
Published: (2025)
by: Tang, Tiffany M., et al.
Published: (2025)
Distributional Counterfactual Explanations With Optimal Transport
by: You, Lei, et al.
Published: (2024)
by: You, Lei, et al.
Published: (2024)
Tabular Diffusion Counterfactual Explanations
by: Zhang, Wei, et al.
Published: (2025)
by: Zhang, Wei, et al.
Published: (2025)
Graph Diffusion Counterfactual Explanation
by: Bechtoldt, David, et al.
Published: (2025)
by: Bechtoldt, David, et al.
Published: (2025)
Counterfactual Explanations for Clustering Models
by: Spagnol, Aurora, et al.
Published: (2024)
by: Spagnol, Aurora, et al.
Published: (2024)
Causality-Aware Local Interpretable Model-Agnostic Explanations
by: Cinquini, Martina, et al.
Published: (2022)
by: Cinquini, Martina, et al.
Published: (2022)
Flexible Counterfactual Explanations with Generative Models
by: Hellemans, Stig, et al.
Published: (2025)
by: Hellemans, Stig, et al.
Published: (2025)
Watermarking Counterfactual Explanations
by: Guo, Hangzhi, et al.
Published: (2024)
by: Guo, Hangzhi, et al.
Published: (2024)
Unity Forests: Improving Interaction Modelling and Interpretability in Random Forests
by: Hornung, Roman, et al.
Published: (2026)
by: Hornung, Roman, et al.
Published: (2026)
A New Approach to Backtracking Counterfactual Explanations: A Unified Causal Framework for Efficient Model Interpretability
by: Fatemi, Pouria, et al.
Published: (2025)
by: Fatemi, Pouria, et al.
Published: (2025)
Learning Interpretable Characteristic Kernels via Decision Forests
by: Panda, Sambit, et al.
Published: (2018)
by: Panda, Sambit, et al.
Published: (2018)
Example-based Explanations for Random Forests using Machine Unlearning
by: Surve, Tanmay, et al.
Published: (2024)
by: Surve, Tanmay, et al.
Published: (2024)
ACE: Adapting sampling for Counterfactual Explanations
by: Guerrero, Margarita A., et al.
Published: (2025)
by: Guerrero, Margarita A., et al.
Published: (2025)
Optimal Transport Group Counterfactual Explanations
by: Valero-Leal, Enrique, et al.
Published: (2026)
by: Valero-Leal, Enrique, et al.
Published: (2026)
Counterfactual Explanations Under Concept Drift
by: Kostrzewa, Marcin, et al.
Published: (2026)
by: Kostrzewa, Marcin, et al.
Published: (2026)
Plausible Counterfactual Explanations of Recommendations
by: Černý, Jakub, et al.
Published: (2025)
by: Černý, Jakub, et al.
Published: (2025)
Counterfactual Explanations for Linear Optimization
by: Kurtz, Jannis, et al.
Published: (2024)
by: Kurtz, Jannis, et al.
Published: (2024)
Surrogate Interpretable Graph for Random Decision Forests
by: Dubey, Akshat, et al.
Published: (2025)
by: Dubey, Akshat, et al.
Published: (2025)
A Comparative Study of DSPy Teleprompter Algorithms for Aligning Large Language Models Evaluation Metrics to Human Evaluation
by: Sarmah, Bhaskarjit, et al.
Published: (2024)
by: Sarmah, Bhaskarjit, et al.
Published: (2024)
Counterfactual Training: Teaching Models Plausible and Actionable Explanations
by: Altmeyer, Patrick, et al.
Published: (2026)
by: Altmeyer, Patrick, et al.
Published: (2026)
Counterfactual Explanations with Probabilistic Guarantees on their Robustness to Model Change
by: Stępka, Ignacy, et al.
Published: (2024)
by: Stępka, Ignacy, et al.
Published: (2024)
Cluster-Based Random Forest Visualization and Interpretation
by: Sondag, Max, et al.
Published: (2025)
by: Sondag, Max, et al.
Published: (2025)
How to Choose a Threshold for an Evaluation Metric for Large Language Models
by: Sarmah, Bhaskarjit, et al.
Published: (2024)
by: Sarmah, Bhaskarjit, et al.
Published: (2024)
Counterfactual Explanations for k-means and Gaussian Clustering
by: Vardakas, Georgios, et al.
Published: (2025)
by: Vardakas, Georgios, et al.
Published: (2025)
CF-OPT: Counterfactual Explanations for Structured Prediction
by: Vivier-Ardisson, Germain, et al.
Published: (2024)
by: Vivier-Ardisson, Germain, et al.
Published: (2024)
Motif-guided Time Series Counterfactual Explanations
by: Li, Peiyu, et al.
Published: (2022)
by: Li, Peiyu, et al.
Published: (2022)
One-for-many Counterfactual Explanations by Column Generation
by: Lodi, Andrea, et al.
Published: (2024)
by: Lodi, Andrea, et al.
Published: (2024)
Similar Items
-
Open Set Recognition for Random Forest
by: Feng, Guanchao, et al.
Published: (2024) -
Explainable Unsupervised Anomaly Detection with Random Forest
by: Harvey, Joshua S., et al.
Published: (2025) -
Quantile Regression using Random Forest Proximities
by: Li, Mingshu, et al.
Published: (2024) -
Enhanced Local Explainability and Trust Scores with Random Forest Proximities
by: Rosaler, Joshua, et al.
Published: (2023) -
Case-based Explainability for Random Forest: Prototypes, Critics, Counter-factuals and Semi-factuals
by: Yampolsky, Gregory, et al.
Published: (2024)