Saved in:
| Main Author: | Ke, Xiaopeng |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2501.01025 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Algorithms for Adversarially Robust Deep Learning
by: Robey, Alexander
Published: (2025)
by: Robey, Alexander
Published: (2025)
Robust Deep Reinforcement Learning against Adversarial Behavior Manipulation
by: Yamabe, Shojiro, et al.
Published: (2024)
by: Yamabe, Shojiro, et al.
Published: (2024)
Robust Adversarial Quantification via Conflict-Aware Evidential Deep Learning
by: Barker, Charmaine, et al.
Published: (2025)
by: Barker, Charmaine, et al.
Published: (2025)
Robust Deep Reinforcement Learning with Adaptive Adversarial Perturbations in Action Space
by: Liu, Qianmei, et al.
Published: (2024)
by: Liu, Qianmei, et al.
Published: (2024)
A Dual-Agent Adversarial Framework for Robust Generalization in Deep Reinforcement Learning
by: Xie, Zhengpeng, et al.
Published: (2025)
by: Xie, Zhengpeng, et al.
Published: (2025)
Robust Deep Reinforcement Learning Through Adversarial Attacks and Training : A Survey
by: Schott, Lucas, et al.
Published: (2024)
by: Schott, Lucas, et al.
Published: (2024)
Enhancing Adversarial Robustness of Deep Neural Networks Through Supervised Contrastive Learning
by: Wang, Longwei, et al.
Published: (2024)
by: Wang, Longwei, et al.
Published: (2024)
Exploring Adversarial Robustness of Deep State Space Models
by: Qi, Biqing, et al.
Published: (2024)
by: Qi, Biqing, et al.
Published: (2024)
Deep Metric Loss for Multimodal Learning
by: Moon, Sehwan, et al.
Published: (2023)
by: Moon, Sehwan, et al.
Published: (2023)
Toward Robust Signed Graph Learning through Joint Input-Target Denoising
by: Wu, Junran, et al.
Published: (2025)
by: Wu, Junran, et al.
Published: (2025)
GJDNet: Robust Graph Neural Networks via Joint Disentangled Learning Against Adversarial Attacks
by: Cui, Canyixing, et al.
Published: (2026)
by: Cui, Canyixing, et al.
Published: (2026)
Adversarial Diffusion for Robust Reinforcement Learning
by: Foffano, Daniele, et al.
Published: (2025)
by: Foffano, Daniele, et al.
Published: (2025)
Maintaining Adversarial Robustness in Continuous Learning
by: Ru, Xiaolei, et al.
Published: (2024)
by: Ru, Xiaolei, et al.
Published: (2024)
Towards Robust Policy: Enhancing Offline Reinforcement Learning with Adversarial Attacks and Defenses
by: Nguyen, Thanh, et al.
Published: (2024)
by: Nguyen, Thanh, et al.
Published: (2024)
Towards Reliable Evaluation of Adversarial Robustness for Spiking Neural Networks
by: Wang, Jihang, et al.
Published: (2025)
by: Wang, Jihang, et al.
Published: (2025)
Towards Robust Deep Reinforcement Learning against Environmental State Perturbation
by: Wang, Chenxu, et al.
Published: (2025)
by: Wang, Chenxu, et al.
Published: (2025)
Adversarial Preference Learning for Robust LLM Alignment
by: Wang, Yuanfu, et al.
Published: (2025)
by: Wang, Yuanfu, et al.
Published: (2025)
Adversarial Signed Graph Learning with Differential Privacy
by: Ke, Haobin, et al.
Published: (2025)
by: Ke, Haobin, et al.
Published: (2025)
Robustness Tokens: Towards Adversarial Robustness of Transformers
by: Pulfer, Brian, et al.
Published: (2025)
by: Pulfer, Brian, et al.
Published: (2025)
Theory Foundation of Physics-Enhanced Residual Learning
by: Liang, Shixiao, et al.
Published: (2025)
by: Liang, Shixiao, et al.
Published: (2025)
On the Implicit Adversariality of Catastrophic Forgetting in Deep Continual Learning
by: Peng, Ze, et al.
Published: (2025)
by: Peng, Ze, et al.
Published: (2025)
VCAT: Vulnerability-aware and Curiosity-driven Adversarial Training for Enhancing Autonomous Vehicle Robustness
by: Cai, Xuan, et al.
Published: (2024)
by: Cai, Xuan, et al.
Published: (2024)
Learning Robust Reasoning through Guided Adversarial Self-Play
by: Li, Shuozhe, et al.
Published: (2026)
by: Li, Shuozhe, et al.
Published: (2026)
Adversarial Robustness Unhardening via Backdoor Attacks in Federated Learning
by: Kim, Taejin, et al.
Published: (2023)
by: Kim, Taejin, et al.
Published: (2023)
Robust Model-Based Reinforcement Learning with an Adversarial Auxiliary Model
by: Herremans, Siemen, et al.
Published: (2024)
by: Herremans, Siemen, et al.
Published: (2024)
Evaluating the Robustness of Deep-Learning Algorithm-Selection Models by Evolving Adversarial Instances
by: Hart, Emma, et al.
Published: (2024)
by: Hart, Emma, et al.
Published: (2024)
Order Acquisition Under Competitive Pressure: A Rapidly Adaptive Reinforcement Learning Approach for Ride-Hailing Subsidy Strategies
by: Shi, Fangzhou, et al.
Published: (2025)
by: Shi, Fangzhou, et al.
Published: (2025)
Adversarially Robust Decision Transformer
by: Tang, Xiaohang, et al.
Published: (2024)
by: Tang, Xiaohang, et al.
Published: (2024)
Impact of Architectural Modifications on Deep Learning Adversarial Robustness
by: Juraev, Firuz, et al.
Published: (2024)
by: Juraev, Firuz, et al.
Published: (2024)
Hierarchical Reference Sets for Robust Unsupervised Detection of Scattered and Clustered Outliers
by: Zhang, Yiqun, et al.
Published: (2026)
by: Zhang, Yiqun, et al.
Published: (2026)
Robust off-policy Reinforcement Learning via Soft Constrained Adversary
by: Nakanishi, Kosuke, et al.
Published: (2024)
by: Nakanishi, Kosuke, et al.
Published: (2024)
Dynamic Adversarial Reinforcement Learning for Robust Multimodal Large Language Models
by: Bao, Yicheng, et al.
Published: (2026)
by: Bao, Yicheng, et al.
Published: (2026)
Towards Interpretable Adversarial Examples via Sparse Adversarial Attack
by: Lin, Fudong, et al.
Published: (2025)
by: Lin, Fudong, et al.
Published: (2025)
Adversarial Examples Might be Avoidable: The Role of Data Concentration in Adversarial Robustness
by: Pal, Ambar, et al.
Published: (2023)
by: Pal, Ambar, et al.
Published: (2023)
How Worst-Case Are Adversarial Attacks? Linking Adversarial and Perturbation Robustness
by: Rossolini, Giulio
Published: (2026)
by: Rossolini, Giulio
Published: (2026)
Mitigating Adversarial Perturbations for Deep Reinforcement Learning via Vector Quantization
by: Luu, Tung M., et al.
Published: (2024)
by: Luu, Tung M., et al.
Published: (2024)
Toward Reliable Tea Leaf Disease Diagnosis Using Deep Learning Model: Enhancing Robustness With Explainable AI and Adversarial Training
by: Ghosh, Samanta, et al.
Published: (2026)
by: Ghosh, Samanta, et al.
Published: (2026)
Robust Decision Aggregation with Adversarial Experts
by: Guo, Yongkang, et al.
Published: (2024)
by: Guo, Yongkang, et al.
Published: (2024)
Adversarial Robustness Overestimation and Instability in TRADES
by: Li, Jonathan Weiping, et al.
Published: (2024)
by: Li, Jonathan Weiping, et al.
Published: (2024)
Bridging Symmetry and Robustness: On the Role of Equivariance in Enhancing Adversarial Robustness
by: Wang, Longwei, et al.
Published: (2025)
by: Wang, Longwei, et al.
Published: (2025)
Similar Items
-
Algorithms for Adversarially Robust Deep Learning
by: Robey, Alexander
Published: (2025) -
Robust Deep Reinforcement Learning against Adversarial Behavior Manipulation
by: Yamabe, Shojiro, et al.
Published: (2024) -
Robust Adversarial Quantification via Conflict-Aware Evidential Deep Learning
by: Barker, Charmaine, et al.
Published: (2025) -
Robust Deep Reinforcement Learning with Adaptive Adversarial Perturbations in Action Space
by: Liu, Qianmei, et al.
Published: (2024) -
A Dual-Agent Adversarial Framework for Robust Generalization in Deep Reinforcement Learning
by: Xie, Zhengpeng, et al.
Published: (2025)