1 citations · 1 across the 9 of their papers we have counts for
11 papers
XAI-Guided Fourier Domain Generalisation for Pure-Simulation Sim-to-Real Deep Reinforcement Learning
Wei Yao, Chuyao Wang, Ziwei Wang +1
Deep reinforcement learning has shown strong potential for vision-based autonomous driving, but policies trained in simulation often suffer severe performance degradation when depl…
Regime-Aware Router Learning for Multi-Domain Medical Image Generation with Diffusion Experts
Yaoshuai Ma, Wei Yao, Yongxiang Liu +5
Heterogeneous medical image data motivate domain-specialized diffusion experts, but expert specialization alone does not determine how their predictions should be combined during s…
On the Blessing of Pre-training in Weak-to-Strong Generalization
Wei Yao, Wang Zhaoyang, Gengze Xu +5
The paradigm of Weak-to-Strong Generalization (W2SG) suggests that a pre-trained strong model can surpass its weak supervisor, yet the decisive role of pre-training remains theoret…
On Weak-to-Strong Generalization and f-Divergence
Wei Yao, Gengze Xu, Huayi Tang +4
Weak-to-strong generalization (W2SG) has emerged as a promising paradigm for stimulating the capabilities of strong pre-trained models by leveraging supervision from weaker supervi…
Weak-to-Strong Generalization via Bregman Bias-Variance Decomposition
Gengze Xu, Wei Yao, Ziqiao Wang +1
Weak-to-strong generalization (W2SG) is the phenomenon in which a powerful student model, trained on labels produced by a weaker teacher, ultimately outperforms the teacher on the…
Revisiting Weak-to-Strong Generalization in Theory and Practice: Reverse KL vs. Forward KL
Wei Yao, Wenkai Yang, Ziqiao Wang +2
As large language models advance toward superhuman performance, ensuring their alignment with human values and abilities grows increasingly complex. Weak-to-strong generalization o…
The Capabilities and Limitations of Weak-to-Strong Generalization: Generalization and Calibration
Wei Yao, Wenkai Yang, Gengze Xu +3
Weak-to-strong generalization, where weakly supervised strong models outperform their weaker teachers, offers a promising approach to aligning superhuman models with human values.…
Understanding Model Ensemble in Transferable Adversarial Attack
Wei Yao, Zeliang Zhang, Huayi Tang +1
Model ensemble adversarial attack has become a powerful method for generating transferable adversarial examples that can target even unknown models, but its theoretical foundation…
Towards Tracing Trustworthiness Dynamics: Revisiting Pre-training Period of Large Language Models
Chen Qian, Jie Zhang, Wei Yao +5
Ensuring the trustworthiness of large language models (LLMs) is crucial. Most studies concentrate on fully pre-trained LLMs to better understand and improve LLMs' trustworthiness.…
Bag of Tricks to Boost Adversarial Transferability
Zeliang Zhang, Wei Yao, Xiaosen Wang
Deep neural networks are widely known to be vulnerable to adversarial examples. However, vanilla adversarial examples generated under the white-box setting often exhibit low transf…
Understanding Fairness Surrogate Functions in Algorithmic Fairness
Wei Yao, Zhanke Zhou, Zhicong Li +2
It has been observed that machine learning algorithms exhibit biased predictions against certain population groups. To mitigate such bias while achieving comparable accuracy, a pro…