From the 1 of 10 linked papers with an AI index.
10 papers
When and Why Does Multi-Agent Debate Fail and Does It Really Underperform?
Yongqiang Chen, Gang Niu, James Cheng +2
The paper examines why multi-agent debate (MAD) often underperforms single-agent methods, identifies flaws in existing competitive and consensus-based MAD protocols, and proposes a…
Learning Locally, Revising Globally: Global Reviser for Federated Learning with Noisy Labels
Yuxin Tian, Mouxing Yang, Yuhao Zhou +5
Conventional federated learning (FL) heavily depends on high-quality labels, which are often impractical in the real world, leading to the federated label-noise (F-LN) problem. Wor…
Reinforcement Learning with Verifiable yet Noisy Rewards under Imperfect Verifiers
Xin-Qiang Cai, Wei Wang, Feng Liu +3
Reinforcement Learning with Verifiable Rewards (RLVR) replaces costly human labeling with automated verifiers. To reduce verifier hacking, many RLVR systems binarize rewards to $\{…
BrokenBind: Universal Modality Exploration beyond Dataset Boundaries
Zhuo Huang, Runnan Chen, Bo Han +3
Multi-modal learning combines various modalities to provide a comprehensive understanding of real-world problems. A common strategy is to directly bind different modalities togethe…
On the Role of Label Noise in the Feature Learning Process
Andi Han, Wei Huang, Zhanpeng Zhou +5
Deep learning with noisy labels presents significant challenges. In this work, we theoretically characterize the role of label noise from a feature learning perspective. Specifical…
Accurate Forgetting for Heterogeneous Federated Continual Learning
Abudukelimu Wuerkaixi, Sen Cui, Jingfeng Zhang +6
Recent years have witnessed a burgeoning interest in federated learning (FL). However, the contexts in which clients engage in sequential learning remain under-explored. Bridging F…