On the Escaping Efficiency of Distributed Adversarial Training Algorithms
Fuente:
arXiv
Saved in:
| Main Authors: | Cao, Ying, Yuan, Kun, Sayed, Ali H. |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Stability and Generalization of Adversarial Diffusion Training
by: Hosseini, Hesam, et al.
Published: (2025)
by: Hosseini, Hesam, et al.
Published: (2025)
On the Trade-off between Flatness and Optimization in Distributed Learning
by: Cao, Ying, et al.
Published: (2024)
by: Cao, Ying, et al.
Published: (2024)
Decentralized Adversarial Training over Graphs
by: Cao, Ying, et al.
Published: (2023)
by: Cao, Ying, et al.
Published: (2023)
Diffusion Learning with Partial Agent Participation and Local Updates
by: Rizk, Elsa, et al.
Published: (2025)
by: Rizk, Elsa, et al.
Published: (2025)
Asynchronous Diffusion Learning with Agent Subsampling and Local Updates
by: Rizk, Elsa, et al.
Published: (2024)
by: Rizk, Elsa, et al.
Published: (2024)
Uniformly Stable Algorithms for Adversarial Training and Beyond
by: Xiao, Jiancong, et al.
Published: (2024)
by: Xiao, Jiancong, et al.
Published: (2024)
Efficient Optimization Algorithms for Linear Adversarial Training
by: RIbeiro, Antônio H., et al.
Published: (2024)
by: RIbeiro, Antônio H., et al.
Published: (2024)
Adversarial Label Invariant Graph Data Augmentations for Out-of-Distribution Generalization
by: Zhang, Simon, et al.
Published: (2026)
by: Zhang, Simon, et al.
Published: (2026)
Distributed Adaptive Learning Under Communication Constraints
by: Carpentiero, Marco, et al.
Published: (2021)
by: Carpentiero, Marco, et al.
Published: (2021)
Dimer-Enhanced Optimization: A First-Order Approach to Escaping Saddle Points in Neural Network Training
by: Hu, Yue, et al.
Published: (2025)
by: Hu, Yue, et al.
Published: (2025)
Adversarial Training Improves Generalization Under Distribution Shifts in Bioacoustics
by: Heinrich, René, et al.
Published: (2025)
by: Heinrich, René, et al.
Published: (2025)
Distributional Adversarial Attacks and Training in Deep Hedging
by: He, Guangyi, et al.
Published: (2025)
by: He, Guangyi, et al.
Published: (2025)
Mixed Monotonicity Reachability Analysis of Neural ODE: A Trade-Off Between Tightness and Efficiency
by: Sayed, Abdelrahman Sayed, et al.
Published: (2025)
by: Sayed, Abdelrahman Sayed, et al.
Published: (2025)
Graph Exploration for Effective Multi-agent Q-Learning
by: Zhaikhan, Ainur, et al.
Published: (2023)
by: Zhaikhan, Ainur, et al.
Published: (2023)
Multi-agent Off-policy Actor-Critic Reinforcement Learning for Partially Observable Environments
by: Zhaikhan, Ainur, et al.
Published: (2024)
by: Zhaikhan, Ainur, et al.
Published: (2024)
On the Efficiency of Training Robust Decision Trees
by: Gerlach, Benedict, et al.
Published: (2025)
by: Gerlach, Benedict, et al.
Published: (2025)
Enhancing Adversarial Robustness via Uncertainty-Aware Distributional Adversarial Training
by: Dong, Junhao, et al.
Published: (2024)
by: Dong, Junhao, et al.
Published: (2024)
Adversarial Effects on Expressibility and Trainability in Distributed Variational Quantum Algorithms
by: Sadhu, Abhishek, et al.
Published: (2026)
by: Sadhu, Abhishek, et al.
Published: (2026)
Improving Fast Adversarial Training Paradigm: An Example Taxonomy Perspective
by: Gui, Jie, et al.
Published: (2024)
by: Gui, Jie, et al.
Published: (2024)
When and Why Adversarial Training Improves PINNs: A Neural Tangent Kernel Perspective
by: Cao, Yuan-dong, et al.
Published: (2026)
by: Cao, Yuan-dong, et al.
Published: (2026)
HERTA: A High-Efficiency and Rigorous Training Algorithm for Unfolded Graph Neural Networks
by: Yang, Yongyi, et al.
Published: (2024)
by: Yang, Yongyi, et al.
Published: (2024)
High-Probability Convergence Guarantees of Decentralized SGD
by: Armacki, Aleksandar, et al.
Published: (2025)
by: Armacki, Aleksandar, et al.
Published: (2025)
Algorithmic Fairness in Performative Policy Learning: Escaping the Impossibility of Group Fairness
by: Somerstep, Seamus, et al.
Published: (2024)
by: Somerstep, Seamus, et al.
Published: (2024)
Margin Discrepancy-based Adversarial Training for Multi-Domain Text Classification
by: Wu, Yuan
Published: (2024)
by: Wu, Yuan
Published: (2024)
On the Impact of Hard Adversarial Instances on Overfitting in Adversarial Training
by: Liu, Chen, et al.
Published: (2021)
by: Liu, Chen, et al.
Published: (2021)
Distributional Adversarial Loss
by: Ahmadi, Saba, et al.
Published: (2024)
by: Ahmadi, Saba, et al.
Published: (2024)
TAET: Two-Stage Adversarial Equalization Training on Long-Tailed Distributions
by: YuHang, Wang, et al.
Published: (2025)
by: YuHang, Wang, et al.
Published: (2025)
On Characterizing Learnability for Adversarial Noisy Bandits
by: Hanneke, Steve, et al.
Published: (2026)
by: Hanneke, Steve, et al.
Published: (2026)
Adversarial Training of Reward Models
by: Bukharin, Alexander, et al.
Published: (2025)
by: Bukharin, Alexander, et al.
Published: (2025)
Criticality Leveraged Adversarial Training (CLAT) for Boosted Performance via Parameter Efficiency
by: Gopal, Bhavna, et al.
Published: (2024)
by: Gopal, Bhavna, et al.
Published: (2024)
Doubly Adaptive Social Learning
by: Carpentiero, Marco, et al.
Published: (2025)
by: Carpentiero, Marco, et al.
Published: (2025)
Escaping the Mode Lottery: Multi-Response Training Improves Language Model Generalization
by: Amin, Hasan, et al.
Published: (2026)
by: Amin, Hasan, et al.
Published: (2026)
Formal Algorithms for Model Efficiency
by: Tyagi, Naman, et al.
Published: (2025)
by: Tyagi, Naman, et al.
Published: (2025)
HGAN-SDEs: Learning Neural Stochastic Differential Equations with Hermite-Guided Adversarial Training
by: Xu, Yuanjian, et al.
Published: (2025)
by: Xu, Yuanjian, et al.
Published: (2025)
Disttack: Graph Adversarial Attacks Toward Distributed GNN Training
by: Zhang, Yuxiang, et al.
Published: (2024)
by: Zhang, Yuxiang, et al.
Published: (2024)
Towards Non-Adversarial Algorithmic Recourse
by: Leemann, Tobias, et al.
Published: (2024)
by: Leemann, Tobias, et al.
Published: (2024)
Achieving Linear Speedup with ProxSkip in Distributed Stochastic Optimization
by: Guo, Luyao, et al.
Published: (2023)
by: Guo, Luyao, et al.
Published: (2023)
Adaptive Meta-learning-based Adversarial Training for Robust Automatic Modulation Classification
by: Bamdad, Amirmohammad, et al.
Published: (2025)
by: Bamdad, Amirmohammad, et al.
Published: (2025)
Escaping Saddle Points for Nonsmooth Weakly Convex Functions via Perturbed Proximal Algorithms
by: Huang, Minhui, et al.
Published: (2021)
by: Huang, Minhui, et al.
Published: (2021)
A Neural Network Training Method Based on Distributed PID Control
by: Kun, Jiang
Published: (2024)
by: Kun, Jiang
Published: (2024)
Similar Items
-
Stability and Generalization of Adversarial Diffusion Training
by: Hosseini, Hesam, et al.
Published: (2025) -
On the Trade-off between Flatness and Optimization in Distributed Learning
by: Cao, Ying, et al.
Published: (2024) -
Decentralized Adversarial Training over Graphs
by: Cao, Ying, et al.
Published: (2023) -
Diffusion Learning with Partial Agent Participation and Local Updates
by: Rizk, Elsa, et al.
Published: (2025) -
Asynchronous Diffusion Learning with Agent Subsampling and Local Updates
by: Rizk, Elsa, et al.
Published: (2024)