Distributional Adversarial Attacks and Training in Deep Hedging
Fuente:
arXiv
Saved in:
| Main Authors: | He, Guangyi, Sutter, Tobias, Gonon, Lukas |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Gradient Regularized Newton Boosting Trees with Global Convergence
by: Zozoulenko, Nikita, et al.
Published: (2026)
by: Zozoulenko, Nikita, et al.
Published: (2026)
A Two-Timescale Primal-Dual Framework for Reinforcement Learning via Online Dual Variable Guidance
by: Wolter, Axel Friedrich, et al.
Published: (2025)
by: Wolter, Axel Friedrich, et al.
Published: (2025)
Regularized Q-learning through Robust Averaging
by: Schmitt-Förster, Peter, et al.
Published: (2024)
by: Schmitt-Förster, Peter, et al.
Published: (2024)
Insights on Muon from Simple Quadratics
by: Gonon, Antoine, et al.
Published: (2026)
by: Gonon, Antoine, et al.
Published: (2026)
Tight Robustness Certificates and Wasserstein Distributional Attacks for Deep Neural Networks
by: Le, Bach C., et al.
Published: (2025)
by: Le, Bach C., et al.
Published: (2025)
Randomized algorithms and PAC bounds for inverse reinforcement learning in continuous spaces
by: Kamoutsi, Angeliki, et al.
Published: (2024)
by: Kamoutsi, Angeliki, et al.
Published: (2024)
Towards Optimal Offline Reinforcement Learning
by: Li, Mengmeng, et al.
Published: (2025)
by: Li, Mengmeng, et al.
Published: (2025)
Wasserstein Distributionally Robust Regret Optimization
by: Fiechtner, Lukas-Benedikt, et al.
Published: (2025)
by: Fiechtner, Lukas-Benedikt, et al.
Published: (2025)
Adversarial Training Should Be Cast as a Non-Zero-Sum Game
by: Robey, Alexander, et al.
Published: (2023)
by: Robey, Alexander, et al.
Published: (2023)
DeepMartingale: Duality of the Optimal Stopping Problem with Expressivity and High-Dimensional Hedging
by: Ye, Junyan, et al.
Published: (2025)
by: Ye, Junyan, et al.
Published: (2025)
Distributionally and Adversarially Robust Logistic Regression via Intersecting Wasserstein Balls
by: Selvi, Aras, et al.
Published: (2024)
by: Selvi, Aras, et al.
Published: (2024)
An Optimal Transport Approach for Computing Adversarial Training Lower Bounds in Multiclass Classification
by: Trillos, Nicolas Garcia, et al.
Published: (2024)
by: Trillos, Nicolas Garcia, et al.
Published: (2024)
Adversarially and Distributionally Robust Virtual Energy Storage Systems via the Scenario Approach
by: Pantazis, Georgios, et al.
Published: (2025)
by: Pantazis, Georgios, et al.
Published: (2025)
Data-driven Reachable Set Estimation with Tunable Adversarial and Wasserstein Distributional Guarantees
by: Pantazis, Georgios, et al.
Published: (2026)
by: Pantazis, Georgios, et al.
Published: (2026)
Adversarial Training of Two-Layer Polynomial and ReLU Activation Networks via Convex Optimization
by: Kuelbs, Daniel, et al.
Published: (2024)
by: Kuelbs, Daniel, et al.
Published: (2024)
Training Deep Learning Models with Norm-Constrained LMOs
by: Pethick, Thomas, et al.
Published: (2025)
by: Pethick, Thomas, et al.
Published: (2025)
Regularized Gradient Clipping Provably Trains Wide and Deep Neural Networks
by: Tucat, Matteo, et al.
Published: (2024)
by: Tucat, Matteo, et al.
Published: (2024)
Exposing the Illusion of Fairness: Auditing Vulnerabilities to Distributional Manipulation Attacks
by: Lafargue, Valentin, et al.
Published: (2025)
by: Lafargue, Valentin, et al.
Published: (2025)
Efficient Optimization Algorithms for Linear Adversarial Training
by: RIbeiro, Antônio H., et al.
Published: (2024)
by: RIbeiro, Antônio H., et al.
Published: (2024)
OTAD: An Optimal Transport-Induced Robust Model for Agnostic Adversarial Attack
by: Gai, Kuo, et al.
Published: (2024)
by: Gai, Kuo, et al.
Published: (2024)
TAMUNA: Doubly Accelerated Distributed Optimization with Local Training, Compression, and Partial Participation
by: Condat, Laurent, et al.
Published: (2023)
by: Condat, Laurent, et al.
Published: (2023)
Policy Gradient Algorithms for Robust MDPs with Non-Rectangular Uncertainty Sets
by: Li, Mengmeng, et al.
Published: (2023)
by: Li, Mengmeng, et al.
Published: (2023)
Adaptive Batch Size Schedules for Distributed Training of Language Models with Data and Model Parallelism
by: Lau, Tim Tsz-Kit, et al.
Published: (2024)
by: Lau, Tim Tsz-Kit, et al.
Published: (2024)
An Adaptive and Stability-Promoting Layerwise Training Approach for Sparse Deep Neural Network Architecture
by: Krishnanunni, C G, et al.
Published: (2022)
by: Krishnanunni, C G, et al.
Published: (2022)
Regularization for Adversarial Robust Learning
by: Wang, Jie, et al.
Published: (2024)
by: Wang, Jie, et al.
Published: (2024)
Analysis of On-policy Policy Gradient Methods under the Distribution Mismatch
by: Wang, Weizhen, et al.
Published: (2025)
by: Wang, Weizhen, et al.
Published: (2025)
On the Duality Between Sharpness-Aware Minimization and Adversarial Training
by: Zhang, Yihao, et al.
Published: (2024)
by: Zhang, Yihao, et al.
Published: (2024)
Decision-Dependent Stochastic Optimization: The Role of Distribution Dynamics
by: He, Zhiyu, et al.
Published: (2025)
by: He, Zhiyu, et al.
Published: (2025)
Adversarially Robust Multitask Adaptive Control
by: Fallah, Kasra, et al.
Published: (2025)
by: Fallah, Kasra, et al.
Published: (2025)
Greedy Low-Rank Gradient Compression for Distributed Learning with Convergence Guarantees
by: Chen, Chuyan, et al.
Published: (2025)
by: Chen, Chuyan, et al.
Published: (2025)
Wasserstein distributional adversarial training for deep neural networks
by: Bai, Xingjian, et al.
Published: (2025)
by: Bai, Xingjian, et al.
Published: (2025)
Training Infinitely Deep and Wide Transformers
by: Barboni, Raphaël, et al.
Published: (2026)
by: Barboni, Raphaël, et al.
Published: (2026)
Parameter-free Algorithms for the Stochastically Extended Adversarial Model
by: Wang, Shuche, et al.
Published: (2025)
by: Wang, Shuche, et al.
Published: (2025)
Bilevel Models for Adversarial Learning and A Case Study
by: Zheng, Yutong, et al.
Published: (2025)
by: Zheng, Yutong, et al.
Published: (2025)
Optimal Algorithms for Online Convex Optimization with Adversarial Constraints
by: Sinha, Abhishek, et al.
Published: (2023)
by: Sinha, Abhishek, et al.
Published: (2023)
Tight Bounds for Online Convex Optimization with Adversarial Constraints
by: Sinha, Abhishek, et al.
Published: (2024)
by: Sinha, Abhishek, et al.
Published: (2024)
An Optimistic Algorithm for Online Convex Optimization with Adversarial Constraints
by: Lekeufack, Jordan, et al.
Published: (2024)
by: Lekeufack, Jordan, et al.
Published: (2024)
Distributionally Robust Optimization with Adversarial Data Contamination
by: Li, Shuyao, et al.
Published: (2025)
by: Li, Shuyao, et al.
Published: (2025)
A Distributed ADMM-based Deep Learning Approach for Thermal Control in Multi-Zone Buildings under Demand Response Events
by: Taboga, Vincent, et al.
Published: (2023)
by: Taboga, Vincent, et al.
Published: (2023)
DR-PETS: Learning-Based Control With Planning in Adversarial Environments
by: Jesawada, Hozefa, et al.
Published: (2025)
by: Jesawada, Hozefa, et al.
Published: (2025)
Similar Items
-
Gradient Regularized Newton Boosting Trees with Global Convergence
by: Zozoulenko, Nikita, et al.
Published: (2026) -
A Two-Timescale Primal-Dual Framework for Reinforcement Learning via Online Dual Variable Guidance
by: Wolter, Axel Friedrich, et al.
Published: (2025) -
Regularized Q-learning through Robust Averaging
by: Schmitt-Förster, Peter, et al.
Published: (2024) -
Insights on Muon from Simple Quadratics
by: Gonon, Antoine, et al.
Published: (2026) -
Tight Robustness Certificates and Wasserstein Distributional Attacks for Deep Neural Networks
by: Le, Bach C., et al.
Published: (2025)