Saved in:
| Main Authors: | van Erven, Tim, Mayo, Jack, Olkhovskaya, Julia, Wei, Chen-Yu |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2508.11931 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Sparse Nonparametric Contextual Bandits
by: Flynn, Hamish, et al.
Published: (2025)
by: Flynn, Hamish, et al.
Published: (2025)
Improved Regret Bounds for Bandits with Expert Advice
by: Cesa-Bianchi, Nicolò, et al.
Published: (2024)
by: Cesa-Bianchi, Nicolò, et al.
Published: (2024)
Kernel-Based Function Approximation for Average Reward Reinforcement Learning: An Optimist No-Regret Algorithm
by: Vakili, Sattar, et al.
Published: (2024)
by: Vakili, Sattar, et al.
Published: (2024)
A Reduction Algorithm for Markovian Contextual Linear Bandits
by: Buyukkalayci, Kaan, et al.
Published: (2026)
by: Buyukkalayci, Kaan, et al.
Published: (2026)
Accelerated Rates between Stochastic and Adversarial Online Convex Optimization
by: Sachs, Sarah, et al.
Published: (2023)
by: Sachs, Sarah, et al.
Published: (2023)
Nearly Minimax Discrete Distribution Estimation in Kullback-Leibler Divergence with High Probability
by: van der Hoeven, Dirk, et al.
Published: (2025)
by: van der Hoeven, Dirk, et al.
Published: (2025)
Kernelized Reinforcement Learning with Order Optimal Regret Bounds
by: Vakili, Sattar, et al.
Published: (2023)
by: Vakili, Sattar, et al.
Published: (2023)
Sample-efficient Learning of Concepts with Theoretical Guarantees: from Data to Concepts without Interventions
by: Fokkema, Hidde, et al.
Published: (2025)
by: Fokkema, Hidde, et al.
Published: (2025)
Practical and Optimal Algorithm for Linear Contextual Bandits with Rare Parameter Updates
by: Yu, Sanghoon, et al.
Published: (2026)
by: Yu, Sanghoon, et al.
Published: (2026)
Best-of-Both-Worlds Algorithms for Linear Contextual Bandits
by: Kuroki, Yuko, et al.
Published: (2023)
by: Kuroki, Yuko, et al.
Published: (2023)
A Simple Reduction Scheme for Constrained Contextual Bandits with Adversarial Contexts via Regression
by: Sarkar, Dhruv, et al.
Published: (2026)
by: Sarkar, Dhruv, et al.
Published: (2026)
The Risks of Recourse in Binary Classification
by: Fokkema, Hidde, et al.
Published: (2023)
by: Fokkema, Hidde, et al.
Published: (2023)
Improved Algorithm for Adversarial Linear Mixture MDPs with Bandit Feedback and Unknown Transition
by: Li, Long-Fei, et al.
Published: (2024)
by: Li, Long-Fei, et al.
Published: (2024)
Nearly Optimal Algorithms for Contextual Dueling Bandits from Adversarial Feedback
by: Di, Qiwei, et al.
Published: (2024)
by: Di, Qiwei, et al.
Published: (2024)
Constrained Contextual Bandits with Adversarial Contexts
by: Sarkar, Dhruv, et al.
Published: (2026)
by: Sarkar, Dhruv, et al.
Published: (2026)
An Online Feasible Point Method for Benign Generalized Nash Equilibrium Problems
by: Sachs, Sarah, et al.
Published: (2024)
by: Sachs, Sarah, et al.
Published: (2024)
Linear Contextual Bandits with Interference
by: Xu, Yang, et al.
Published: (2024)
by: Xu, Yang, et al.
Published: (2024)
Scaling Federated Linear Contextual Bandits via Sketching
by: Yang, Hantao, et al.
Published: (2026)
by: Yang, Hantao, et al.
Published: (2026)
Online Newton Method for Bandit Convex Optimisation
by: Fokkema, Hidde, et al.
Published: (2024)
by: Fokkema, Hidde, et al.
Published: (2024)
Improved Algorithms for Nash Welfare in Linear Bandits
by: Sarkar, Dhruv, et al.
Published: (2026)
by: Sarkar, Dhruv, et al.
Published: (2026)
Contextual Linear Bandits with Delay as Payoff
by: Zhang, Mengxiao, et al.
Published: (2025)
by: Zhang, Mengxiao, et al.
Published: (2025)
Strategic Linear Contextual Bandits
by: Buening, Thomas Kleine, et al.
Published: (2024)
by: Buening, Thomas Kleine, et al.
Published: (2024)
Efficient Algorithms for Logistic Contextual Slate Bandits with Bandit Feedback
by: Goyal, Tanmay, et al.
Published: (2025)
by: Goyal, Tanmay, 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)
by: Tani, Naoto, et al.
Published: (2026)
Active Learning for Stochastic Contextual Linear Bandits
by: Brunskill, Emma, et al.
Published: (2026)
by: Brunskill, Emma, et al.
Published: (2026)
Partially Observable Contextual Bandits with Linear Payoffs
by: Zeng, Sihan, et al.
Published: (2024)
by: Zeng, Sihan, et al.
Published: (2024)
Navigating Sparsities in High-Dimensional Linear Contextual Bandits
by: Zhao, Rui, et al.
Published: (2025)
by: Zhao, Rui, et al.
Published: (2025)
How Does Variance Shape the Regret in Contextual Bandits?
by: Jia, Zeyu, et al.
Published: (2024)
by: Jia, Zeyu, et al.
Published: (2024)
Single Index Bandits: Generalized Linear Contextual Bandits with Unknown Reward Functions
by: Kang, Yue, et al.
Published: (2025)
by: Kang, Yue, et al.
Published: (2025)
Optimal and Practical Batched Linear Bandit Algorithm
by: Yu, Sanghoon, et al.
Published: (2025)
by: Yu, Sanghoon, et al.
Published: (2025)
Federated Linear Contextual Bandits with Heterogeneous Clients
by: Blaser, Ethan, et al.
Published: (2024)
by: Blaser, Ethan, et al.
Published: (2024)
Conservative Contextual Bandits: Beyond Linear Representations
by: Deb, Rohan, et al.
Published: (2024)
by: Deb, Rohan, et al.
Published: (2024)
Linear Contextual Bandits with Hybrid Payoff: Revisited
by: Das, Nirjhar, et al.
Published: (2024)
by: Das, Nirjhar, et al.
Published: (2024)
Thompson Sampling for Multi-Objective Linear Contextual Bandit
by: Park, Somangchan, et al.
Published: (2025)
by: Park, Somangchan, et al.
Published: (2025)
A Jointly Efficient and Optimal Algorithm for Heteroskedastic Generalized Linear Bandits with Adversarial Corruptions
by: Kim, Sanghwa, et al.
Published: (2026)
by: Kim, Sanghwa, 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)
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)
Learning with Incomplete Context: Linear Contextual Bandits with Pretrained Imputation
by: Yan, Hao, et al.
Published: (2025)
by: Yan, Hao, et al.
Published: (2025)
Direction-Aware Offline-to-Online Learning in Linear Contextual Bandits
by: Han, Zean, et al.
Published: (2026)
by: Han, Zean, et al.
Published: (2026)
Online Continuous Hyperparameter Optimization for Generalized Linear Contextual Bandits
by: Kang, Yue, et al.
Published: (2023)
by: Kang, Yue, et al.
Published: (2023)
Similar Items
-
Sparse Nonparametric Contextual Bandits
by: Flynn, Hamish, et al.
Published: (2025) -
Improved Regret Bounds for Bandits with Expert Advice
by: Cesa-Bianchi, Nicolò, et al.
Published: (2024) -
Kernel-Based Function Approximation for Average Reward Reinforcement Learning: An Optimist No-Regret Algorithm
by: Vakili, Sattar, et al.
Published: (2024) -
A Reduction Algorithm for Markovian Contextual Linear Bandits
by: Buyukkalayci, Kaan, et al.
Published: (2026) -
Accelerated Rates between Stochastic and Adversarial Online Convex Optimization
by: Sachs, Sarah, et al.
Published: (2023)