Merging versus Ensembling in Multi-Study Prediction: Theoretical Insight from Random Effects
Fuente:
arXiv
Saved in:
| Main Authors: | Guan, Zoe, Parmigiani, Giovanni, Patil, Prasad |
|---|---|
| Format: | Preprint |
| Published: |
2019
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Multi-Study R-Learner for Estimating Heterogeneous Treatment Effects Across Studies Using Statistical Machine Learning
by: Shyr, Cathy, et al.
Published: (2023)
by: Shyr, Cathy, et al.
Published: (2023)
Cross-Cluster Weighted Forests
by: Ramchandran, Maya, et al.
Published: (2021)
by: Ramchandran, Maya, et al.
Published: (2021)
Debiased Machine Learning for Conformal Prediction of Counterfactual Outcomes Under Runtime Confounding
by: Barnatchez, Keith, et al.
Published: (2026)
by: Barnatchez, Keith, et al.
Published: (2026)
Multi-Task Learning for Sparsity Pattern Heterogeneity: Statistical and Computational Perspectives
by: Behdin, Kayhan, et al.
Published: (2022)
by: Behdin, Kayhan, et al.
Published: (2022)
Toward Theoretical Insights into Diffusion Trajectory Distillation via Operator Merging
by: Gao, Weiguo, et al.
Published: (2025)
by: Gao, Weiguo, et al.
Published: (2025)
Cross-validation Approaches for Multi-study Predictions
by: Ren, Boyu, et al.
Published: (2020)
by: Ren, Boyu, et al.
Published: (2020)
Emergence of Structure in Ensembles of Random Neural Networks
by: Muscarnera, Luca, et al.
Published: (2025)
by: Muscarnera, Luca, et al.
Published: (2025)
Merging Multi-Task Models via Weight-Ensembling Mixture of Experts
by: Tang, Anke, et al.
Published: (2024)
by: Tang, Anke, et al.
Published: (2024)
Theoretical Limitations of Ensembles in the Age of Overparameterization
by: Dern, Niclas, et al.
Published: (2024)
by: Dern, Niclas, et al.
Published: (2024)
FlexiDrop: Theoretical Insights and Practical Advances in Random Dropout Method on GNNs
by: Zhou, Zhiheng, et al.
Published: (2024)
by: Zhou, Zhiheng, et al.
Published: (2024)
Trade-offs in Ensembling, Merging and Routing Among Parameter-Efficient Experts
by: Lotfi, Sanae, et al.
Published: (2026)
by: Lotfi, Sanae, et al.
Published: (2026)
Efficient and Effective Weight-Ensembling Mixture of Experts for Multi-Task Model Merging
by: Shen, Li, et al.
Published: (2024)
by: Shen, Li, et al.
Published: (2024)
Multi-model Ensemble Conformal Prediction in Dynamic Environments
by: Hajihashemi, Erfan, et al.
Published: (2024)
by: Hajihashemi, Erfan, et al.
Published: (2024)
Pareto Merging: Multi-Objective Optimization for Preference-Aware Model Merging
by: Chen, Weiyu, et al.
Published: (2024)
by: Chen, Weiyu, et al.
Published: (2024)
Ensemble Prediction of Task Affinity for Efficient Multi-Task Learning
by: Ayman, Afiya, et al.
Published: (2026)
by: Ayman, Afiya, et al.
Published: (2026)
Why Do More Experts Fail? A Theoretical Analysis of Model Merging
by: Wang, Zijing, et al.
Published: (2025)
by: Wang, Zijing, et al.
Published: (2025)
Permutation Randomization on Nonsmooth Nonconvex Optimization: A Theoretical and Experimental Study
by: Zhang, Wei, et al.
Published: (2025)
by: Zhang, Wei, et al.
Published: (2025)
Enhancing Credit Risk Prediction: A Multi-stage Ensemble Pipeline
by: Wang, Haibo, et al.
Published: (2025)
by: Wang, Haibo, et al.
Published: (2025)
Merge and Guide: Unifying Model Merging and Guided Decoding for Controllable Multi-Objective Generation
by: Xie, Guofu, et al.
Published: (2025)
by: Xie, Guofu, et al.
Published: (2025)
Theoretical Insights into Overparameterized Models in Multi-Task and Replay-Based Continual Learning
by: Banayeeanzade, Amin, et al.
Published: (2024)
by: Banayeeanzade, Amin, et al.
Published: (2024)
Bridging the Theoretical Gap in Randomized Smoothing
by: Delattre, Blaise, et al.
Published: (2025)
by: Delattre, Blaise, et al.
Published: (2025)
Representations Shape Weak-to-Strong Generalization: Theoretical Insights and Empirical Predictions
by: Xue, Yihao, et al.
Published: (2025)
by: Xue, Yihao, et al.
Published: (2025)
Theoretical Insights for Diffusion Guidance: A Case Study for Gaussian Mixture Models
by: Wu, Yuchen, et al.
Published: (2024)
by: Wu, Yuchen, et al.
Published: (2024)
Evaluating Prediction Uncertainty Estimates from BatchEnsemble
by: Blørstad, Morten, et al.
Published: (2026)
by: Blørstad, Morten, et al.
Published: (2026)
Merging and Disentangling Views in Visual Reinforcement Learning for Robotic Manipulation
by: Almuzairee, Abdulaziz, et al.
Published: (2025)
by: Almuzairee, Abdulaziz, et al.
Published: (2025)
Theoretical Study of Conflict-Avoidant Multi-Objective Reinforcement Learning
by: Wang, Yudan, et al.
Published: (2024)
by: Wang, Yudan, et al.
Published: (2024)
Pathologies of Predictive Diversity in Deep Ensembles
by: Abe, Taiga, et al.
Published: (2023)
by: Abe, Taiga, et al.
Published: (2023)
SSAM: Singular Subspace Alignment for Merging Multimodal Large Language Models
by: Reza, Md Kaykobad, et al.
Published: (2026)
by: Reza, Md Kaykobad, et al.
Published: (2026)
Multi-Model Ensemble and Reservoir Computing for River Discharge Prediction in Ungauged Basins
by: Funato, Mizuki, et al.
Published: (2025)
by: Funato, Mizuki, et al.
Published: (2025)
Retrieve, Merge, Predict: Augmenting Tables with Data Lakes
by: Cappuzzo, Riccardo, et al.
Published: (2024)
by: Cappuzzo, Riccardo, et al.
Published: (2024)
Wine Quality Prediction with Ensemble Trees: A Unified, Leak-Free Comparative Study
by: Chen, Zilang
Published: (2025)
by: Chen, Zilang
Published: (2025)
Multi-Level Collaboration in Model Merging
by: Li, Qi, et al.
Published: (2025)
by: Li, Qi, et al.
Published: (2025)
AdaMerging: Adaptive Model Merging for Multi-Task Learning
by: Yang, Enneng, et al.
Published: (2023)
by: Yang, Enneng, et al.
Published: (2023)
Mitigating the Backdoor Effect for Multi-Task Model Merging via Safety-Aware Subspace
by: Yang, Jinluan, et al.
Published: (2024)
by: Yang, Jinluan, et al.
Published: (2024)
FedBEns: One-Shot Federated Learning based on Bayesian Ensemble
by: Talpini, Jacopo, et al.
Published: (2025)
by: Talpini, Jacopo, et al.
Published: (2025)
Empirical Likelihood for Random Forests and Ensembles
by: Chiang, Harold D., et al.
Published: (2025)
by: Chiang, Harold D., et al.
Published: (2025)
Theoretical Guarantees for Causal Discovery on Large Random Graphs
by: Chevalley, Mathieu, et al.
Published: (2025)
by: Chevalley, Mathieu, et al.
Published: (2025)
Towards the Effect of Examples on In-Context Learning: A Theoretical Case Study
by: He, Pengfei, et al.
Published: (2024)
by: He, Pengfei, et al.
Published: (2024)
Efficient Ensemble Conditional Independence Test Framework for Causal Discovery
by: Guan, Zhengkang, et al.
Published: (2025)
by: Guan, Zhengkang, et al.
Published: (2025)
Effect of Random Learning Rate: Theoretical Analysis of SGD Dynamics in Non-Convex Optimization via Stationary Distribution
by: Yoshida, Naoki, et al.
Published: (2024)
by: Yoshida, Naoki, et al.
Published: (2024)
Similar Items
-
Multi-Study R-Learner for Estimating Heterogeneous Treatment Effects Across Studies Using Statistical Machine Learning
by: Shyr, Cathy, et al.
Published: (2023) -
Cross-Cluster Weighted Forests
by: Ramchandran, Maya, et al.
Published: (2021) -
Debiased Machine Learning for Conformal Prediction of Counterfactual Outcomes Under Runtime Confounding
by: Barnatchez, Keith, et al.
Published: (2026) -
Multi-Task Learning for Sparsity Pattern Heterogeneity: Statistical and Computational Perspectives
by: Behdin, Kayhan, et al.
Published: (2022) -
Toward Theoretical Insights into Diffusion Trajectory Distillation via Operator Merging
by: Gao, Weiguo, et al.
Published: (2025)