Cover Image

Robust Distribution Learning with Local and Global Adversarial Corruptions

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Nietert, Sloan, Goldfeld, Ziv, Shafiee, Soroosh
Format:	Preprint
Published:	2024
Subjects:	Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Estimation of Stochastic Optimal Transport Maps
by: Nietert, Sloan, et al.
Published: (2025)

Robust Estimation under the Wasserstein Distance
by: Nietert, Sloan, et al.
Published: (2023)

Robust Alignment via Partial Gromov-Wasserstein Distances
by: Gong, Xiaoyun, et al.
Published: (2025)

Wasserstein Distributionally Robust Online Learning
by: Chen, Guixian, et al.
Published: (2026)

Distributionally Robust Optimization
by: Kuhn, Daniel, et al.
Published: (2024)

Wasserstein Distributionally Robust Estimation in High Dimensions: Performance Analysis and Optimal Hyperparameter Tuning
by: Aolaritei, Liviu, et al.
Published: (2022)

Nash Equilibria, Regularization and Computation in Optimal Transport-Based Distributionally Robust Optimization
by: Shafiee, Soroosh, et al.
Published: (2023)

PLOT: Progressive Localization via Optimal Transport in Neural Causal Abstraction
by: Chang, Jonathn, et al.
Published: (2026)

Information-Theoretic Generalization Bounds for Deep Neural Networks
by: He, Haiyun, et al.
Published: (2024)

Scalable First-order Method for Certifying Optimal k-Sparse GLMs
by: Liu, Jiachang, et al.
Published: (2025)

GPU-friendly and Linearly Convergent First-order Methods for Certifying Optimal $k$-sparse GLMs
by: Liu, Jiachang, et al.
Published: (2026)

Learning in Stackelberg Games with Non-myopic Agents
by: Haghtalab, Nika, et al.
Published: (2022)

Contextual Dynamic Pricing with Heterogeneous Buyers
by: Lykouris, Thodoris, et al.
Published: (2025)

Sliced Inner Product Gromov-Wasserstein Distances
by: Gong, Xiaoyun, et al.
Published: (2026)

Cascading Bandits Robust to Adversarial Corruptions
by: Xie, Jize, et al.
Published: (2025)

Optimal Robust Estimation under Local and Global Corruptions: Stronger Adversary and Smaller Error
by: Pittas, Thanasis, et al.
Published: (2024)

Neural Estimation for Scaling Entropic Multimarginal Optimal Transport
by: Tsur, Dor, et al.
Published: (2025)

Towards Robust Model-Based Reinforcement Learning Against Adversarial Corruption
by: Ye, Chenlu, et al.
Published: (2024)

Performance Guarantees for Quantum Neural Estimation of Entropies
by: Sreekumar, Sreejith, et al.
Published: (2025)

Multi-Agent Stochastic Bandits Robust to Adversarial Corruptions
by: Ghaffari, Fatemeh, et al.
Published: (2024)

NeuroShield: A Neuro-Symbolic Framework for Adversarial Robustness
by: Sarvestani, Ali Shafiee, et al.
Published: (2026)

Quantum Pufferfish Privacy: A Flexible Privacy Framework for Quantum Systems
by: Nuradha, Theshani, et al.
Published: (2023)

Wasserstein Distributionally Robust Optimization: Theory and Applications in Machine Learning
by: Kuhn, Daniel, et al.
Published: (2019)

Learning with Monotone Adversarial Corruptions
by: Larsen, Kasper Green, et al.
Published: (2026)

Distributionally Robust Set Representation Learning Under Inference-Time Element Corruption
by: Chen, Yankai, et al.
Published: (2026)

On Corruption-Robustness in Performative Reinforcement Learning
by: Pollatos, Vasilis, et al.
Published: (2025)

Quantum Neural Estimation of Entropies
by: Goldfeld, Ziv, et al.
Published: (2023)

Sparse Offline Reinforcement Learning with Corruption Robustness
by: Tran, Nam Phuong, et al.
Published: (2025)

Optimal Transportation and Alignment Between Gaussian Measures
by: Dandapanthula, Sanjit, et al.
Published: (2025)

Robust and Computationally Efficient Linear Contextual Bandits under Adversarial Corruption and Heavy-Tailed Noise
by: Tani, Naoto, et al.
Published: (2026)

A Near-optimal, Scalable and Parallelizable Framework for Stochastic Bandits Robust to Adversarial Corruptions and Beyond
by: Hu, Zicheng, et al.
Published: (2025)

Robust Linear Dueling Bandits with Post-serving Context under Unknown Delays and Adversarial Corruptions
by: Oh, Youngmin
Published: (2026)

Robust Reinforcement Learning from Corrupted Human Feedback
by: Bukharin, Alexander, et al.
Published: (2024)

Gradient Flows and Riemannian Structure in the Gromov-Wasserstein Geometry
by: Zhang, Zhengxin, et al.
Published: (2024)

Stress-Testing ML Pipelines with Adversarial Data Corruption
by: Zhu, Jiongli, et al.
Published: (2025)

Disagreement-Regularized Importance Sampling for Adversarial Label Corruption
by: Horváth, Csongor, et al.
Published: (2026)

Learning to Compress: Local Rank and Information Compression in Deep Neural Networks
by: Patel, Niket, et al.
Published: (2024)

Corruption-Robust Offline Reinforcement Learning with General Function Approximation
by: Ye, Chenlu, et al.
Published: (2023)

A Model Selection Approach for Corruption Robust Reinforcement Learning
by: Wei, Chen-Yu, et al.
Published: (2021)

Robust Bayesian Optimisation with Unbounded Corruptions
by: Ezzerg, Abdelhamid, et al.
Published: (2025)