7 citations · 18 across the 5 of their papers we have counts for
5 papers
Uncertainty-Penalized Reinforcement Learning from Human Feedback with Diverse Reward LoRA Ensembles
Yuanzhao Zhai, Han Zhang, Yu Lei +5
Reinforcement learning from human feedback (RLHF) emerges as a promising paradigm for aligning large language models (LLMs). However, a notable challenge in RLHF is overoptimizatio…
Bridging Code Semantic and LLMs: Semantic Chain-of-Thought Prompting for Code Generation
Yingwei Ma, Yue Yu, Shanshan Li +5
Large language models (LLMs) have showcased remarkable prowess in code generation. However, automated code generation is still challenging since it requires a high-level semantic m…
Robust Multi-Agent Reinforcement Learning via Adversarial Regularization: Theoretical Foundation and Stable Algorithms
Alexander Bukharin, Yan Li, Yue Yu +6
Multi-Agent Reinforcement Learning (MARL) has shown promising results across several domains. Despite this promise, MARL policies often lack robustness and are therefore sensitive…
Stochastic Clustered Federated Learning
Dun Zeng, Xiangjing Hu, Shiyu Liu +3
Federated learning is a distributed learning framework that takes full advantage of private data samples kept on edge devices. In real-world federated learning systems, these data…
NeuronFair: Interpretable White-Box Fairness Testing through Biased Neuron Identification
Haibin Zheng, Zhiqing Chen, Tianyu Du +6
Deep neural networks (DNNs) have demonstrated their outperformance in various domains. However, it raises a social concern whether DNNs can produce reliable and fair decisions espe…