M-Loss: Quantifying Model Merging Compatibility with Limited Unlabeled Data
Fuente:
arXiv
Saved in:
| Main Authors: | Wang, Tiantong, Duan, Yiyang, Chen, Haoyu, Wu, Tiantong, Lim, Wei Yang Bryan |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
MPU: Towards Secure and Privacy-Preserving Knowledge Unlearning for Large Language Models
by: Wang, Tiantong, et al.
Published: (2026)
by: Wang, Tiantong, et al.
Published: (2026)
Oblivionis: A Lightweight Learning and Unlearning Framework for Federated Large Language Models
by: Zhang, Fuyao, et al.
Published: (2025)
by: Zhang, Fuyao, et al.
Published: (2025)
Enhancing Federated Domain Adaptation with Multi-Domain Prototype-Based Federated Fine-Tuning
by: Zhang, Jingyuan, et al.
Published: (2024)
by: Zhang, Jingyuan, et al.
Published: (2024)
LoD: Loss-difference OOD Detection by Intentionally Label-Noisifying Unlabeled Wild Data
by: Geng, Chuanxing, et al.
Published: (2025)
by: Geng, Chuanxing, et al.
Published: (2025)
Robust Amortized Bayesian Inference with Self-Consistency Losses on Unlabeled Data
by: Mishra, Aayush, et al.
Published: (2025)
by: Mishra, Aayush, et al.
Published: (2025)
On Leveraging Unlabeled Data for Concurrent Positive-Unlabeled Classification and Robust Generation
by: Yu, Bing, et al.
Published: (2020)
by: Yu, Bing, et al.
Published: (2020)
Learning from M-Tuple Dominant Positive and Unlabeled Data
by: Qin, Jiahe, et al.
Published: (2025)
by: Qin, Jiahe, et al.
Published: (2025)
One-dimensional polarization-hybrid photonic crystal molecules
by: Li, Tiantong, et al.
Published: (2026)
by: Li, Tiantong, et al.
Published: (2026)
The indivisibility of a quantum-corrected AdS black hole with phantom global monopoles
by: Cheng, Tiantong, et al.
Published: (2026)
by: Cheng, Tiantong, et al.
Published: (2026)
Learning from Uncertain Similarity and Unlabeled Data
by: Wei, Meng, et al.
Published: (2025)
by: Wei, Meng, et al.
Published: (2025)
PUAL: A Classifier on Trifurcate Positive-Unlabeled Data
by: Wang, Xiaoke, et al.
Published: (2024)
by: Wang, Xiaoke, et al.
Published: (2024)
Semi-Supervised Crowd Counting from Unlabeled Data
by: Duan, Haoran, et al.
Published: (2021)
by: Duan, Haoran, et al.
Published: (2021)
Adapting to Shifting Correlations with Unlabeled Data Calibration
by: Nguyen, Minh, et al.
Published: (2024)
by: Nguyen, Minh, et al.
Published: (2024)
Deep Positive-Unlabeled Anomaly Detection for Contaminated Unlabeled Data
by: Takahashi, Hiroshi, et al.
Published: (2024)
by: Takahashi, Hiroshi, et al.
Published: (2024)
Unlabeled Data Can Provably Enhance In-Context Learning of Transformers
by: Liu, Renpu, et al.
Published: (2026)
by: Liu, Renpu, et al.
Published: (2026)
Re-Evaluating the Impact of Unseen-Class Unlabeled Data on Semi-Supervised Learning Model
by: He, Rundong, et al.
Published: (2025)
by: He, Rundong, et al.
Published: (2025)
Proper Learnability and the Role of Unlabeled Data
by: Asilis, Julian, et al.
Published: (2025)
by: Asilis, Julian, et al.
Published: (2025)
Positive and Unlabeled Data: Model, Estimation, Inference, and Classification
by: Liu, Siyan, et al.
Published: (2024)
by: Liu, Siyan, et al.
Published: (2024)
Extra-Merge: Tracing the Rank-1 Subspace of Model Merging in Language Model Pre-Training
by: Zhou, Wenjie, et al.
Published: (2026)
by: Zhou, Wenjie, et al.
Published: (2026)
Augmenting Offline RL with Unlabeled Data
by: Wang, Zhao, et al.
Published: (2024)
by: Wang, Zhao, et al.
Published: (2024)
Unlabeled Data vs. Pre-trained Knowledge: Rethinking SSL in the Era of Large Models
by: Lv, Song-Lin, et al.
Published: (2025)
by: Lv, Song-Lin, et al.
Published: (2025)
Collaborative Unlabeled Data Optimization
by: Shang, Xinyi, et al.
Published: (2025)
by: Shang, Xinyi, et al.
Published: (2025)
Model Merging on Loss Landscape: A Geometry Perspective
by: Lu, Juanwu, et al.
Published: (2026)
by: Lu, Juanwu, et al.
Published: (2026)
Accessible, Realistic, and Fair Evaluation of Positive-Unlabeled Learning Algorithms
by: Wang, Wei, et al.
Published: (2025)
by: Wang, Wei, et al.
Published: (2025)
Coupled Training with Privileged Information and Unlabeled Data
by: Shi, Jiahao, et al.
Published: (2026)
by: Shi, Jiahao, et al.
Published: (2026)
LAUD: Integrating Large Language Models with Active Learning for Unlabeled Data
by: Chou, Tzu-Hsuan, et al.
Published: (2025)
by: Chou, Tzu-Hsuan, et al.
Published: (2025)
Heterogeneous Domain Adaptation with Positive and Unlabeled Data
by: Mori, Junki, et al.
Published: (2023)
by: Mori, Junki, et al.
Published: (2023)
Out-Of-Domain Unlabeled Data Improves Generalization
by: Saberi, Amir Hossein, et al.
Published: (2023)
by: Saberi, Amir Hossein, et al.
Published: (2023)
Offline Reinforcement Learning with Domain-Unlabeled Data
by: Nishimori, Soichiro, et al.
Published: (2024)
by: Nishimori, Soichiro, et al.
Published: (2024)
Sample-Optimal Agnostic Boosting with Unlabeled Data
by: Ghai, Udaya, et al.
Published: (2025)
by: Ghai, Udaya, et al.
Published: (2025)
CATCHFed: Efficient Unlabeled Data Utilization for Semi-Supervised Federated Learning in Limited Labels Environments
by: Park, Byoungjun, et al.
Published: (2025)
by: Park, Byoungjun, et al.
Published: (2025)
Merging Smarter, Generalizing Better: Enhancing Model Merging on OOD Data
by: Zhang, Bingjie, et al.
Published: (2025)
by: Zhang, Bingjie, et al.
Published: (2025)
Mix Data or Merge Models? Balancing the Helpfulness, Honesty, and Harmlessness of Large Language Model via Model Merging
by: Yang, Jinluan, et al.
Published: (2025)
by: Yang, Jinluan, et al.
Published: (2025)
MergeMix: Optimizing Mid-Training Data Mixtures via Learnable Model Merging
by: Wang, Jiapeng, et al.
Published: (2026)
by: Wang, Jiapeng, et al.
Published: (2026)
AllMatch: Exploiting All Unlabeled Data for Semi-Supervised Learning
by: Wu, Zhiyu, et al.
Published: (2024)
by: Wu, Zhiyu, et al.
Published: (2024)
Positive-Unlabeled Reinforcement Learning Distillation for On-Premise Small Models
by: Kou, Zhiqiang, et al.
Published: (2026)
by: Kou, Zhiqiang, et al.
Published: (2026)
Heterogeneous Multisource Transfer Learning via Model Averaging for Positive-Unlabeled Data
by: Liu, Jialei, et al.
Published: (2025)
by: Liu, Jialei, et al.
Published: (2025)
How to Craft Backdoors with Unlabeled Data Alone?
by: Wang, Yifei, et al.
Published: (2024)
by: Wang, Yifei, et al.
Published: (2024)
Positive-Unlabeled Diffusion Models for Preventing Sensitive Data Generation
by: Takahashi, Hiroshi, et al.
Published: (2025)
by: Takahashi, Hiroshi, et al.
Published: (2025)
FedAnchor: Enhancing Federated Semi-Supervised Learning with Label Contrastive Loss for Unlabeled Clients
by: Qiu, Xinchi, et al.
Published: (2024)
by: Qiu, Xinchi, et al.
Published: (2024)
Similar Items
-
MPU: Towards Secure and Privacy-Preserving Knowledge Unlearning for Large Language Models
by: Wang, Tiantong, et al.
Published: (2026) -
Oblivionis: A Lightweight Learning and Unlearning Framework for Federated Large Language Models
by: Zhang, Fuyao, et al.
Published: (2025) -
Enhancing Federated Domain Adaptation with Multi-Domain Prototype-Based Federated Fine-Tuning
by: Zhang, Jingyuan, et al.
Published: (2024) -
LoD: Loss-difference OOD Detection by Intentionally Label-Noisifying Unlabeled Wild Data
by: Geng, Chuanxing, et al.
Published: (2025) -
Robust Amortized Bayesian Inference with Self-Consistency Losses on Unlabeled Data
by: Mishra, Aayush, et al.
Published: (2025)