Saved in:
| Main Author: | Salem, Tareq Si |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2601.19867 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Beyond Primal-Dual Methods in Bandits with Stochastic and Adversarial Constraints
by: Bernasconi, Martino, et al.
Published: (2024)
by: Bernasconi, Martino, et al.
Published: (2024)
Online Submodular Maximization via Online Convex Optimization
by: Salem, Tareq Si, et al.
Published: (2023)
by: Salem, Tareq Si, et al.
Published: (2023)
KVCompose: Efficient Structured KV Cache Compression with Composite Tokens
by: Akulov, Dmitry, et al.
Published: (2025)
by: Akulov, Dmitry, et al.
Published: (2025)
Federated Learning for Collaborative Inference Systems: The Case of Early Exit Networks
by: Kaplan, Caelin, et al.
Published: (2024)
by: Kaplan, Caelin, et al.
Published: (2024)
Adversarial Multi-dueling Bandits
by: Gajane, Pratik
Published: (2024)
by: Gajane, Pratik
Published: (2024)
Adversarial Bandit over Bandits: Hierarchical Bandits for Online Configuration Management
by: Avin, Chen, et al.
Published: (2025)
by: Avin, Chen, 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)
Constrained Contextual Bandits with Adversarial Contexts
by: Sarkar, Dhruv, et al.
Published: (2026)
by: Sarkar, Dhruv, et al.
Published: (2026)
Adversarial Combinatorial Bandits with Switching Costs
by: Dong, Yanyan, et al.
Published: (2024)
by: Dong, Yanyan, et al.
Published: (2024)
Faster Rates for Private Adversarial Bandits
by: Asi, Hilal, et al.
Published: (2025)
by: Asi, Hilal, et al.
Published: (2025)
Cascading Bandits Robust to Adversarial Corruptions
by: Xie, Jize, et al.
Published: (2025)
by: Xie, Jize, et al.
Published: (2025)
Stochastic Bandits Robust to Adversarial Attacks
by: Wang, Xuchuang, et al.
Published: (2024)
by: Wang, Xuchuang, et al.
Published: (2024)
Adversarial Bandits against Arbitrary Strategies
by: Kim, Jung-hun, et al.
Published: (2022)
by: Kim, Jung-hun, et al.
Published: (2022)
Goal-Oriented Time-Series Forecasting: Foundation Framework Design
by: Fechete, Luca-Andrei, et al.
Published: (2025)
by: Fechete, Luca-Andrei, et al.
Published: (2025)
Pure Exploration in Bandits with Linear Constraints
by: Carlsson, Emil, et al.
Published: (2023)
by: Carlsson, Emil, et al.
Published: (2023)
Sparse Linear Bandits with Blocking Constraints
by: Jain, Adit, et al.
Published: (2024)
by: Jain, Adit, et al.
Published: (2024)
Contextual Bandits with Stage-wise Constraints
by: Pacchiano, Aldo, et al.
Published: (2024)
by: Pacchiano, Aldo, et al.
Published: (2024)
Nearly-Optimal Algorithm for Adversarial Kernelized Bandits
by: Iwazaki, Shogo
Published: (2026)
by: Iwazaki, Shogo
Published: (2026)
Near-Optimal Regret in Adversarial Kernel Bandits
by: Zhang, Yu-Jie, et al.
Published: (2026)
by: Zhang, Yu-Jie, et al.
Published: (2026)
Sparsity-Agnostic Linear Bandits with Adaptive Adversaries
by: Jin, Tianyuan, et al.
Published: (2024)
by: Jin, Tianyuan, et al.
Published: (2024)
Distributed Linear Bandits under Communication Constraints
by: Salgia, Sudeep, et al.
Published: (2022)
by: Salgia, Sudeep, et al.
Published: (2022)
Multi-Agent Stochastic Bandits Robust to Adversarial Corruptions
by: Ghaffari, Fatemeh, et al.
Published: (2024)
by: Ghaffari, Fatemeh, et al.
Published: (2024)
Neural Contextual Bandits Under Delayed Feedback Constraints
by: Moghimi, Mohammadali, et al.
Published: (2025)
by: Moghimi, Mohammadali, et al.
Published: (2025)
Adaptive Action Duration with Contextual Bandits for Deep Reinforcement Learning in Dynamic Environments
by: Verma, Abhishek, et al.
Published: (2025)
by: Verma, Abhishek, et al.
Published: (2025)
Adaptive Budgeted Multi-Armed Bandits for IoT with Dynamic Resource Constraints
by: Vaishnav, Shubham, et al.
Published: (2025)
by: Vaishnav, Shubham, et al.
Published: (2025)
Adversarial Bandit Optimization with Globally Bounded Perturbations to Linear Losses
by: Cheng, Zhuoyu, et al.
Published: (2026)
by: Cheng, Zhuoyu, et al.
Published: (2026)
Adversarial Bandits with Multi-User Delayed Feedback: Theory and Application
by: Li, Yandi, et al.
Published: (2023)
by: Li, Yandi, et al.
Published: (2023)
An Improved Algorithm for Adversarial Linear Contextual Bandits via Reduction
by: van Erven, Tim, et al.
Published: (2025)
by: van Erven, Tim, et al.
Published: (2025)
Slowly Changing Adversarial Bandit Algorithms are Efficient for Discounted MDPs
by: Kash, Ian A., et al.
Published: (2022)
by: Kash, Ian A., et al.
Published: (2022)
Adversarial Attacks on Combinatorial Multi-Armed Bandits
by: Balasubramanian, Rishab, et al.
Published: (2023)
by: Balasubramanian, Rishab, et al.
Published: (2023)
Efficient Adversarial Attacks on High-dimensional Offline Bandits
by: Hosseini, Seyed Mohammad Hadi, et al.
Published: (2026)
by: Hosseini, Seyed Mohammad Hadi, et al.
Published: (2026)
Stealthy Adversarial Attacks on Stochastic Multi-Armed Bandits
by: Wang, Zhiwei, et al.
Published: (2024)
by: Wang, Zhiwei, et al.
Published: (2024)
HyperArm Bandit Optimization: A Novel approach to Hyperparameter Optimization and an Analysis of Bandit Algorithms in Stochastic and Adversarial Settings
by: Karroum, Samih, et al.
Published: (2025)
by: Karroum, Samih, et al.
Published: (2025)
Beating Adversarial Low-Rank MDPs with Unknown Transition and Bandit Feedback
by: Liu, Haolin, et al.
Published: (2024)
by: Liu, Haolin, et al.
Published: (2024)
Adapting to Stochastic and Adversarial Losses in Episodic MDPs with Aggregate Bandit Feedback
by: Ito, Shinji, et al.
Published: (2025)
by: Ito, Shinji, et al.
Published: (2025)
Heterogeneous Multi-Player Multi-Armed Bandits Robust To Adversarial Attacks
by: Magesh, Akshayaa, et al.
Published: (2025)
by: Magesh, Akshayaa, et al.
Published: (2025)
Nearly Optimal Algorithms for Contextual Dueling Bandits from Adversarial Feedback
by: Di, Qiwei, et al.
Published: (2024)
by: Di, Qiwei, et al.
Published: (2024)
Online Conformal Abstention for Factuality Control Under Adversarial Bandit Feedback
by: Lee, Minjae, et al.
Published: (2025)
by: Lee, Minjae, et al.
Published: (2025)
Near Optimal Adversarial Attacks on Stochastic Bandits and Defenses with Smoothed Responses
by: Zuo, Shiliang
Published: (2020)
by: Zuo, Shiliang
Published: (2020)
Multi-Armed Bandits with Minimum Aggregated Revenue Constraints
by: Yahmed, Ahmed Ben, et al.
Published: (2025)
by: Yahmed, Ahmed Ben, et al.
Published: (2025)
Similar Items
-
Beyond Primal-Dual Methods in Bandits with Stochastic and Adversarial Constraints
by: Bernasconi, Martino, et al.
Published: (2024) -
Online Submodular Maximization via Online Convex Optimization
by: Salem, Tareq Si, et al.
Published: (2023) -
KVCompose: Efficient Structured KV Cache Compression with Composite Tokens
by: Akulov, Dmitry, et al.
Published: (2025) -
Federated Learning for Collaborative Inference Systems: The Case of Early Exit Networks
by: Kaplan, Caelin, et al.
Published: (2024) -
Adversarial Multi-dueling Bandits
by: Gajane, Pratik
Published: (2024)