Testing the Feasibility of Linear Programs with Bandit Feedback
Fuente:
arXiv
Saved in:
| Main Authors: | Gangrade, Aditya, Gopalan, Aditya, Saligrama, Venkatesh, Scott, Clayton |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Safe Linear Bandits over Unknown Polytopes
by: Gangrade, Aditya, et al.
Published: (2022)
by: Gangrade, Aditya, et al.
Published: (2022)
Constrained Linear Thompson Sampling
by: Gangrade, Aditya, et al.
Published: (2025)
by: Gangrade, Aditya, et al.
Published: (2025)
Universal Inference Meets Random Projections: A Scalable Test for Log-concavity
by: Dunn, Robin, et al.
Published: (2021)
by: Dunn, Robin, et al.
Published: (2021)
Linear Transformers Implicitly Discover Unified Numerical Algorithms
by: Lutz, Patrick, et al.
Published: (2025)
by: Lutz, Patrick, et al.
Published: (2025)
Data Deletion Can Help in Adaptive RL
by: Budhraja, Param, et al.
Published: (2026)
by: Budhraja, Param, et al.
Published: (2026)
Symmetry Reveals Layerwise Dynamics: How Transformers Perform In-Context Classification
by: Lutz, Patrick, et al.
Published: (2026)
by: Lutz, Patrick, et al.
Published: (2026)
Optimal Batched Linear Bandits
by: Ren, Xuanfei, et al.
Published: (2024)
by: Ren, Xuanfei, et al.
Published: (2024)
Truncated LinUCB for Stochastic Linear Bandits
by: Song, Yanglei, et al.
Published: (2022)
by: Song, Yanglei, et al.
Published: (2022)
Label Noise: Ignorance Is Bliss
by: Zhu, Yilun, et al.
Published: (2024)
by: Zhu, Yilun, 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)
The Sample Complexity of Multiple Change Point Identification under Bandit Feedback
by: Graf, Maximilian, et al.
Published: (2026)
by: Graf, Maximilian, et al.
Published: (2026)
FLIPHAT: Joint Differential Privacy for High Dimensional Sparse Linear Bandits
by: Chakraborty, Sunrit, et al.
Published: (2024)
by: Chakraborty, Sunrit, et al.
Published: (2024)
Minimax Rate-Optimal Algorithms for High-Dimensional Stochastic Linear Bandits
by: Liu, Jingyu, et al.
Published: (2025)
by: Liu, Jingyu, et al.
Published: (2025)
Avoiding the Price of Adaptivity: Inference in Linear Contextual Bandits via Stability
by: Praharaj, Samya, et al.
Published: (2025)
by: Praharaj, Samya, et al.
Published: (2025)
Max-Linear Regression by Convex Programming
by: Kim, Seonho, et al.
Published: (2021)
by: Kim, Seonho, et al.
Published: (2021)
Domain Generalization Under Posterior Drift
by: Zhu, Yilun, et al.
Published: (2025)
by: Zhu, Yilun, et al.
Published: (2025)
Batched Nonparametric Contextual Bandits
by: Jiang, Rong, et al.
Published: (2024)
by: Jiang, Rong, et al.
Published: (2024)
The Fragility of Optimized Bandit Algorithms
by: Fan, Lin, et al.
Published: (2021)
by: Fan, Lin, et al.
Published: (2021)
PAC Learning with Bandit Feedback: Sharp Sample Complexity in the Realizable Setting
by: Hanneke, Steve, et al.
Published: (2026)
by: Hanneke, Steve, et al.
Published: (2026)
Bad Values but Good Behavior: Learning Highly Misspecified Bandits and MDPs
by: Banerjee, Debangshu, et al.
Published: (2023)
by: Banerjee, Debangshu, et al.
Published: (2023)
Adaptive Smooth Non-Stationary Bandits
by: Suk, Joe
Published: (2024)
by: Suk, Joe
Published: (2024)
Transfer Learning for Contextual Multi-armed Bandits
by: Cai, Changxiao, et al.
Published: (2022)
by: Cai, Changxiao, et al.
Published: (2022)
Multitask Learning and Bandits via Robust Statistics
by: Xu, Kan, et al.
Published: (2021)
by: Xu, Kan, et al.
Published: (2021)
Multimodal Bandits: Regret Lower Bounds and Optimal Algorithms
by: Réveillard, William, et al.
Published: (2025)
by: Réveillard, William, et al.
Published: (2025)
Design Experiments to Compare Multi-armed Bandit Algorithms
by: Meng, Huiling, et al.
Published: (2026)
by: Meng, Huiling, et al.
Published: (2026)
Multiple Testing of Linear Forms for Noisy Matrix Completion
by: Ma, Wanteng, et al.
Published: (2023)
by: Ma, Wanteng, et al.
Published: (2023)
Online Clustering of Data Sequences with Bandit Information
by: Chandran, G Dhinesh, et al.
Published: (2025)
by: Chandran, G Dhinesh, et al.
Published: (2025)
Asymptotically Optimal Problem-Dependent Bandit Policies for Transfer Learning
by: Prevost, Adrien, et al.
Published: (2025)
by: Prevost, Adrien, et al.
Published: (2025)
Multi-Armed Bandits With Machine Learning-Generated Surrogate Rewards
by: Ji, Wenlong, et al.
Published: (2025)
by: Ji, Wenlong, et al.
Published: (2025)
On Instability of Minimax Optimal Optimism-Based Bandit Algorithms
by: Praharaj, Samya, et al.
Published: (2025)
by: Praharaj, Samya, et al.
Published: (2025)
Towards Efficient and Optimal Covariance-Adaptive Algorithms for Combinatorial Semi-Bandits
by: Zhou, Julien, et al.
Published: (2024)
by: Zhou, Julien, et al.
Published: (2024)
The Adaptivity Barrier in Batched Nonparametric Bandits: Sharp Characterization of the Price of Unknown Margin
by: Jiang, Rong, et al.
Published: (2025)
by: Jiang, Rong, et al.
Published: (2025)
Upper Counterfactual Confidence Bounds: a New Optimism Principle for Contextual Bandits
by: Xu, Yunbei, et al.
Published: (2020)
by: Xu, Yunbei, et al.
Published: (2020)
Statistical Complexity and Optimal Algorithms for Non-linear Ridge Bandits
by: Rajaraman, Nived, et al.
Published: (2023)
by: Rajaraman, Nived, et al.
Published: (2023)
Diffusion Posterior Sampling is Computationally Intractable
by: Gupta, Shivam, et al.
Published: (2024)
by: Gupta, Shivam, et al.
Published: (2024)
Extended UCB Policies for Multi-armed Bandit Problems
by: Liu, Keqin, et al.
Published: (2011)
by: Liu, Keqin, et al.
Published: (2011)
A Simple and Optimal Policy Design with Safety against Heavy-Tailed Risk for Stochastic Bandits
by: Simchi-Levi, David, et al.
Published: (2022)
by: Simchi-Levi, David, et al.
Published: (2022)
Choosing the Better Bandit Algorithm under Data Sharing: When Do A/B Experiments Work?
by: Li, Shuangning, et al.
Published: (2025)
by: Li, Shuangning, et al.
Published: (2025)
Concentrated Differential Privacy for Bandits
by: Azize, Achraf, et al.
Published: (2023)
by: Azize, Achraf, et al.
Published: (2023)
Concave Statistical Utility Maximization Bandits via Influence-Function Gradients
by: Carrasco, Matías, et al.
Published: (2026)
by: Carrasco, Matías, et al.
Published: (2026)
Similar Items
-
Safe Linear Bandits over Unknown Polytopes
by: Gangrade, Aditya, et al.
Published: (2022) -
Constrained Linear Thompson Sampling
by: Gangrade, Aditya, et al.
Published: (2025) -
Universal Inference Meets Random Projections: A Scalable Test for Log-concavity
by: Dunn, Robin, et al.
Published: (2021) -
Linear Transformers Implicitly Discover Unified Numerical Algorithms
by: Lutz, Patrick, et al.
Published: (2025) -
Data Deletion Can Help in Adaptive RL
by: Budhraja, Param, et al.
Published: (2026)