:: Library Catalog

Saved in:

Bibliographic Details
Main Authors:	Yin, Qichuan, Su, Ziwei, Li, Shuangning
Format:	Preprint
Published:	2026
Subjects:	Machine Learning
Online Access:	https://arxiv.org/abs/2603.27049
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Differentially Private Model Merging
by: Yin, Qichuan, et al.
Published: (2026)

Choosing the Better Bandit Algorithm under Data Sharing: When Do A/B Experiments Work?
by: Li, Shuangning, et al.
Published: (2025)

Distribution-Free Fair Federated Learning with Small Samples
by: Yin, Qichuan, et al.
Published: (2024)

Loss-Guided Auxiliary Agents for Overcoming Mode Collapse in GFlowNets
by: Malek, Idriss, et al.
Published: (2025)

MaxSup: Overcoming Representation Collapse in Label Smoothing
by: Zhou, Yuxuan, et al.
Published: (2025)

On the Robustness of Neural Collapse and the Neural Collapse of Robustness
by: Su, Jingtong, et al.
Published: (2023)

When Adaptation Fails: A Gradient-Based Diagnosis of Collapsed Gating in Vision-Language Prompt Learning
by: Fang, Yunxuan, et al.
Published: (2026)

Dyadic Reinforcement Learning
by: Li, Shuangning, et al.
Published: (2023)

CoSIFL: Collaborative Secure and Incentivized Federated Learning with Differential Privacy
by: Xie, Zhanhong, et al.
Published: (2025)

Preventing Model Collapse in Gaussian Process Latent Variable Models
by: Li, Ying, et al.
Published: (2024)

Overcoming Data and Model Heterogeneities in Decentralized Federated Learning via Synthetic Anchors
by: Huang, Chun-Yin, et al.
Published: (2024)

When Does Model Collapse Occur in Structured Interactive Learning?
by: Wu, Yuchen, et al.
Published: (2026)

Two Facets of the Same Optimization Coin: Model Degradation and Representation Collapse in Graph Foundation Models
by: Li, Xunkai, et al.
Published: (2025)

Incentivized Lipschitz Bandits
by: Chakraborty, Sourav, et al.
Published: (2025)

Overcoming Overfitting in Reinforcement Learning via Gaussian Process Diffusion Policy
by: Horprasert, Amornyos, et al.
Published: (2025)

Persistence Paradox in Dynamic Science
by: Bao, Honglin, et al.
Published: (2025)

Incentives in Private Collaborative Machine Learning
by: Sim, Rachael Hwee Ling, et al.
Published: (2024)

Understanding DNNs in Feature Interaction Models: A Dimensional Collapse Perspective
by: Wang, Jiancheng, et al.
Published: (2026)

On the Algorithmic Bias of Aligning Large Language Models with RLHF: Preference Collapse and Matching Regularization
by: Xiao, Jiancong, et al.
Published: (2024)

EvoCoT: Overcoming the Exploration Bottleneck in Reinforcement Learning
by: Liu, Huanyu, et al.
Published: (2025)

Strong Model Collapse
by: Dohmatob, Elvis, et al.
Published: (2024)

Geometry Meets Incentives: Sample-Efficient Incentivized Exploration with Linear Contexts
by: Schiffer, Benjamin, et al.
Published: (2025)

Proof-of-Learning with Incentive Security
by: Zhao, Zishuo, et al.
Published: (2024)

Rethinking Continual Learning with Progressive Neural Collapse
by: Wang, Zheng, et al.
Published: (2025)

RAGEN-2: Reasoning Collapse in Agentic RL
by: Wang, Zihan, et al.
Published: (2026)

Incentivized Truthful Communication for Federated Bandits
by: Wei, Zhepei, et al.
Published: (2024)

The Multi-Query Paradox in Zeroth-Order Optimization
by: Lin, Wei, et al.
Published: (2025)

Incentive Aware AI Regulations: A Credal Characterisation
by: Singh, Anurag, et al.
Published: (2026)

Deep Learning for Protein Complex Prediction and Design
by: Xie, Ziwei
Published: (2026)

Posterior Collapse as a Phase Transition in Variational Autoencoders
by: Li, Zhen, et al.
Published: (2025)

AI Alignment via Incentives and Correction
by: Agarwal, Rohit, et al.
Published: (2026)

Incentivizing LLMs to Self-Verify Their Answers
by: Zhang, Fuxiang, et al.
Published: (2025)

Deeper with Riemannian Geometry: Overcoming Oversmoothing and Oversquashing for Graph Foundation Models
by: Sun, Li, et al.
Published: (2025)

Reward Collapse in Aligning Large Language Models
by: Song, Ziang, et al.
Published: (2023)

Neural Collapse in Multi-label Learning with Pick-all-label Loss
by: Li, Pengyu, et al.
Published: (2023)

On the Calibration of Epistemic Uncertainty: Principles, Paradoxes and Conflictual Loss
by: Fellaji, Mohammed, et al.
Published: (2024)

Overcoming the Modality Gap in Context-Aided Forecasting
by: Zheng, Vincent Zhihao, et al.
Published: (2026)

Overcoming the Challenges of Batch Normalization in Federated Learning
by: Guerraoui, Rachid, et al.
Published: (2024)

Overcoming Domain Drift in Online Continual Learning
by: Lyu, Fan, et al.
Published: (2024)

Meaningful Causal Aggregation and Paradoxical Confounding
by: Zhu, Yuchen, et al.
Published: (2023)