Saved in:
| Main Authors: | Cotorobai, Alexandre, Silva, Jorge Miguel, Oliveira, Jose Luis |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2505.08085 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Principled Federated Random Forests for Heterogeneous Data
by: Khellaf, Rémi, et al.
Published: (2026)
by: Khellaf, Rémi, et al.
Published: (2026)
Conditional Distribution Quantization in Machine Learning
by: Delattre, Blaise, et al.
Published: (2025)
by: Delattre, Blaise, et al.
Published: (2025)
Federated Random Forest for Partially Overlapping Clinical Data
by: Park, Youngjun, et al.
Published: (2024)
by: Park, Youngjun, et al.
Published: (2024)
Invariant Random Forest: Tree-Based Model Solution for OOD Generalization
by: Liao, Yufan, et al.
Published: (2023)
by: Liao, Yufan, et al.
Published: (2023)
A Secure and Private Distributed Bayesian Federated Learning Design
by: Yang, Nuocheng, et al.
Published: (2026)
by: Yang, Nuocheng, et al.
Published: (2026)
FederatedRSF : Federated Random Survival Forests for Partially Overlapping Medical Data
by: Moradpour, Maryam, et al.
Published: (2026)
by: Moradpour, Maryam, et al.
Published: (2026)
DynFrs: An Efficient Framework for Machine Unlearning in Random Forest
by: Wang, Shurong, et al.
Published: (2024)
by: Wang, Shurong, et al.
Published: (2024)
MMD-based Variable Importance for Distributional Random Forest
by: Bénard, Clément, et al.
Published: (2023)
by: Bénard, Clément, et al.
Published: (2023)
Distributed and Secure Kernel-Based Quantum Machine Learning
by: Swaminathan, Arjhun, et al.
Published: (2024)
by: Swaminathan, Arjhun, et al.
Published: (2024)
Random Forest Autoencoders for Guided Representation Learning
by: Aumon, Adrien, et al.
Published: (2025)
by: Aumon, Adrien, et al.
Published: (2025)
Example-based Explanations for Random Forests using Machine Unlearning
by: Surve, Tanmay, et al.
Published: (2024)
by: Surve, Tanmay, et al.
Published: (2024)
Noninvasive Acute Compartment Syndrome Diagnosis Using Random Forest Machine Learning
by: Hweij, Zaina Abu, et al.
Published: (2024)
by: Hweij, Zaina Abu, et al.
Published: (2024)
Random Forest Calibration
by: Shaker, Mohammad Hossein, et al.
Published: (2025)
by: Shaker, Mohammad Hossein, et al.
Published: (2025)
Heterogeneous Random Forest
by: Kim, Ye-eun, et al.
Published: (2024)
by: Kim, Ye-eun, et al.
Published: (2024)
Interpretable Machine Learning for Life Expectancy Prediction: A Comparative Study of Linear Regression, Decision Tree, and Random Forest
by: Dolgopolyi, Roman, et al.
Published: (2025)
by: Dolgopolyi, Roman, et al.
Published: (2025)
Perfectly-Private Analog Secure Aggregation in Federated Learning
by: Jaramillo-Velez, Delio, et al.
Published: (2025)
by: Jaramillo-Velez, Delio, et al.
Published: (2025)
Federated Learning for Cross-Domain Data Privacy: A Distributed Approach to Secure Collaboration
by: Zhang, Yiwei, et al.
Published: (2025)
by: Zhang, Yiwei, et al.
Published: (2025)
FedGT: Identification of Malicious Clients in Federated Learning with Secure Aggregation
by: Xhemrishi, Marvin, et al.
Published: (2023)
by: Xhemrishi, Marvin, et al.
Published: (2023)
Jacobian Aligned Random Forests
by: Rauniyar, Sarwesh
Published: (2025)
by: Rauniyar, Sarwesh
Published: (2025)
Random Similarity Isolation Forests
by: Chwilczyński, Sebastian, et al.
Published: (2025)
by: Chwilczyński, Sebastian, et al.
Published: (2025)
Improving Random Forests by Smoothing
by: Liu, Ziyi, et al.
Published: (2025)
by: Liu, Ziyi, et al.
Published: (2025)
Neural Random Forest Imitation
by: Reinders, Christoph, et al.
Published: (2019)
by: Reinders, Christoph, et al.
Published: (2019)
DiNo and RanBu: Lightweight Predictions from Shallow Random Forests
by: Santos, Tiago Mendonça dos, et al.
Published: (2025)
by: Santos, Tiago Mendonça dos, et al.
Published: (2025)
Lassoed Forests: Random Forests with Adaptive Lasso Post-selection
by: Shang, Jing, et al.
Published: (2025)
by: Shang, Jing, et al.
Published: (2025)
Distributional Random Forests for Complex Survey Designs on Reproducing Kernel Hilbert Spaces
by: Zou, Yating, et al.
Published: (2025)
by: Zou, Yating, et al.
Published: (2025)
Exogenous Randomness Empowering Random Forests
by: Mei, Tianxing, et al.
Published: (2024)
by: Mei, Tianxing, et al.
Published: (2024)
Autoencoding Random Forests
by: Vu, Binh Duc, et al.
Published: (2025)
by: Vu, Binh Duc, et al.
Published: (2025)
Hyperbolic Random Forests
by: Doorenbos, Lars, et al.
Published: (2023)
by: Doorenbos, Lars, et al.
Published: (2023)
Random Forest-Based Prediction of Stroke Outcome
by: Fernandez-Lozano, Carlos, et al.
Published: (2024)
by: Fernandez-Lozano, Carlos, et al.
Published: (2024)
Forest-Guided Clustering -- Shedding Light into the Random Forest Black Box
by: Sousa, Lisa Barros de Andrade e, et al.
Published: (2025)
by: Sousa, Lisa Barros de Andrade e, et al.
Published: (2025)
When do Random Forests work?
by: Revelas, C., et al.
Published: (2025)
by: Revelas, C., et al.
Published: (2025)
Grafting: Making Random Forests Consistent
by: Waltz, Nicholas
Published: (2024)
by: Waltz, Nicholas
Published: (2024)
Prediction Error Estimation in Random Forests
by: Krupkin, Ian, et al.
Published: (2023)
by: Krupkin, Ian, et al.
Published: (2023)
Random Forest-Supervised Manifold Alignment
by: Rhodes, Jake S., et al.
Published: (2024)
by: Rhodes, Jake S., et al.
Published: (2024)
Open Set Recognition for Random Forest
by: Feng, Guanchao, et al.
Published: (2024)
by: Feng, Guanchao, et al.
Published: (2024)
SFPDML: Securer and Faster Privacy-Preserving Distributed Machine Learning based on MKTFHE
by: Wang, Hongxiao, et al.
Published: (2022)
by: Wang, Hongxiao, et al.
Published: (2022)
Random Forest Weighted Local Fréchet Regression with Random Objects
by: Qiu, Rui, et al.
Published: (2022)
by: Qiu, Rui, et al.
Published: (2022)
Preserving Privacy and Security in Federated Learning
by: Nguyen, Truc, et al.
Published: (2022)
by: Nguyen, Truc, et al.
Published: (2022)
Interpolation-Driven Machine Learning Approaches for Plume Shine Dose Estimation: A Comparison of XGBoost, Random Forest, and TabNet
by: Sadhu, Biswajit, et al.
Published: (2026)
by: Sadhu, Biswajit, et al.
Published: (2026)
DisAgg: Distributed Aggregators for Efficient Secure Aggregation in Federated Learning
by: Mehmood, Haaris, et al.
Published: (2026)
by: Mehmood, Haaris, et al.
Published: (2026)
Similar Items
-
Principled Federated Random Forests for Heterogeneous Data
by: Khellaf, Rémi, et al.
Published: (2026) -
Conditional Distribution Quantization in Machine Learning
by: Delattre, Blaise, et al.
Published: (2025) -
Federated Random Forest for Partially Overlapping Clinical Data
by: Park, Youngjun, et al.
Published: (2024) -
Invariant Random Forest: Tree-Based Model Solution for OOD Generalization
by: Liao, Yufan, et al.
Published: (2023) -
A Secure and Private Distributed Bayesian Federated Learning Design
by: Yang, Nuocheng, et al.
Published: (2026)