18 citations · 36 across the 21 of their papers we have counts for
13 papers · 1 filter
Dummy-Aware Weighted Attack (DAWA): Breaking the Safe Sink in Dummy Class Defenses
Yunrui Yu, Xuxiang Feng, Pengda Qin +5
Adversarial robustness evaluation faces a critical challenge as new defense paradigms emerge that can exploit limitations in existing assessment methods. This paper reveals that Du…
Why the Maximum Second Derivative of Activations Matters for Adversarial Robustness
Yunrui Yu, Hang Su, Jun Zhu
This work investigates the critical role of activation function curvature -- quantified by the maximum second derivative -- in adversarial robustness. Using the Recursi…
Helix: Evolutionary Reinforcement Learning for Open-Ended Scientific Problem Solving
Chang Su, Zhongkai Hao, Zhizhou Zhang +4
Large language models (LLMs) with reasoning abilities have demonstrated growing promise for tackling complex scientific problems. Yet such tasks are inherently domain-specific, unb…
Safety Alignment as Continual Learning: Mitigating the Alignment Tax via Orthogonal Gradient Projection
Guanglong Sun, Siyuan Zhang, Liyuan Wang +3
Safety post-training can improve the harmfulness and policy compliance of Large Language Models (LLMs), but it may also reduce general utility, a phenomenon often described as the…
Exploratory Diffusion Model for Unsupervised Reinforcement Learning
Chengyang Ying, Huayu Chen, Xinning Zhou +3
Unsupervised reinforcement learning (URL) aims to pre-train agents by exploring diverse states or skills in reward-free environments, facilitating efficient adaptation to downstrea…
ManiBox: Enhancing Embodied Spatial Generalization via Scalable Simulation Data Generations
Hengkai Tan, Xuezhou Xu, Chengyang Ying +7
Embodied agents require robust spatial intelligence to execute precise real-world manipulations. However, this remains a significant challenge, as current methods often struggle to…
HiDe-PET: Continual Learning via Hierarchical Decomposition of Parameter-Efficient Tuning
Liyuan Wang, Jingyi Xie, Xingxing Zhang +2
The deployment of pre-trained models (PTMs) has greatly advanced the field of continual learning (CL), enabling positive knowledge transfer and resilience to catastrophic forgettin…
Fourier Controller Networks for Real-Time Decision-Making in Embodied Learning
Hengkai Tan, Songming Liu, Kai Ma +4
Transformer has shown promise in reinforcement learning to model time-varying features for obtaining generalized low-level robot policies on diverse robotics datasets in embodied l…
PEAC: Unsupervised Pre-training for Cross-Embodiment Reinforcement Learning
Chengyang Ying, Zhongkai Hao, Xinning Zhou +4
Designing generalizable agents capable of adapting to diverse embodiments has achieved significant attention in Reinforcement Learning (RL), which is critical for deploying RL agen…
Overcoming Recency Bias of Normalization Statistics in Continual Learning: Balance and Adaptation
Yilin Lyu, Liyuan Wang, Xingxing Zhang +4
Continual learning entails learning a sequence of tasks and balancing their knowledge appropriately. With limited access to old training samples, much of the current work in deep n…
Hierarchical Decomposition of Prompt-Based Continual Learning: Rethinking Obscured Sub-optimality
Liyuan Wang, Jingyi Xie, Xingxing Zhang +3
Prompt-based continual learning is an emerging direction in leveraging pre-trained knowledge for downstream continual learning, and has almost reached the performance pinnacle unde…
Towards a General Framework for Continual Learning with Pre-training
Liyuan Wang, Jingyi Xie, Xingxing Zhang +2
In this work, we present a general framework for continual learning of sequentially arrived tasks with the use of pre-training, which has emerged as a promising direction for artif…
Towards Safe Reinforcement Learning via Constraining Conditional Value-at-Risk
Chengyang Ying, Xinning Zhou, Hang Su +3
Though deep reinforcement learning (DRL) has obtained substantial success, it may encounter catastrophic failures due to the intrinsic uncertainty of both transition and observatio…