Asymptotically Optimal Problem-Dependent Bandit Policies for Transfer Learning
Fuente:
arXiv
Guardado en:
| Autores principales: | Prevost, Adrien, Mathieu, Timothee, Maillard, Odalric-Ambrym |
|---|---|
| Formato: | Preprint |
| Publicado: |
2025
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
Leveraging priors on distribution functions for multi-arm bandits
por: Vashishtha, Sumit, et al.
Publicado: (2025)
por: Vashishtha, Sumit, et al.
Publicado: (2025)
How Hard is it to Confuse a World Model?
por: Radji, Waris, et al.
Publicado: (2025)
por: Radji, Waris, et al.
Publicado: (2025)
The regret lower bound for communicating Markov Decision Processes
por: Boone, Victor, et al.
Publicado: (2025)
por: Boone, Victor, et al.
Publicado: (2025)
The Confusing Instance Principle for Online Linear Quadratic Control
por: Radji, Waris, et al.
Publicado: (2025)
por: Radji, Waris, et al.
Publicado: (2025)
Hierarchical Subspaces of Policies for Continual Offline Reinforcement Learning
por: Kobanda, Anthony, et al.
Publicado: (2024)
por: Kobanda, Anthony, et al.
Publicado: (2024)
A Continual Offline Reinforcement Learning Benchmark for Navigation Tasks
por: Kobanda, Anthony, et al.
Publicado: (2025)
por: Kobanda, Anthony, et al.
Publicado: (2025)
How to Shrink Confidence Sets for Many Equivalent Discrete Distributions?
por: Maillard, Odalric-Ambrym, et al.
Publicado: (2024)
por: Maillard, Odalric-Ambrym, et al.
Publicado: (2024)
Offline Goal-Conditioned Reinforcement Learning with Projective Quasimetric Planning
por: Kobanda, Anthony, et al.
Publicado: (2025)
por: Kobanda, Anthony, et al.
Publicado: (2025)
Transfer Learning for Contextual Multi-armed Bandits
por: Cai, Changxiao, et al.
Publicado: (2022)
por: Cai, Changxiao, et al.
Publicado: (2022)
Extended UCB Policies for Multi-armed Bandit Problems
por: Liu, Keqin, et al.
Publicado: (2011)
por: Liu, Keqin, et al.
Publicado: (2011)
Optimal Batched Linear Bandits
por: Ren, Xuanfei, et al.
Publicado: (2024)
por: Ren, Xuanfei, et al.
Publicado: (2024)
A Simple and Optimal Policy Design with Safety against Heavy-Tailed Risk for Stochastic Bandits
por: Simchi-Levi, David, et al.
Publicado: (2022)
por: Simchi-Levi, David, et al.
Publicado: (2022)
Pliable rejection sampling
por: Erraqabi, Akram, et al.
Publicado: (2026)
por: Erraqabi, Akram, et al.
Publicado: (2026)
AdaStop: adaptive statistical testing for sound comparisons of Deep RL agents
por: Mathieu, Timothée, et al.
Publicado: (2023)
por: Mathieu, Timothée, et al.
Publicado: (2023)
Multimodal Bandits: Regret Lower Bounds and Optimal Algorithms
por: Réveillard, William, et al.
Publicado: (2025)
por: Réveillard, William, et al.
Publicado: (2025)
Non-Asymptotic Analysis of Data Augmentation for Precision Matrix Estimation
por: Morisset, Lucas, et al.
Publicado: (2025)
por: Morisset, Lucas, et al.
Publicado: (2025)
Set-Valued Policy Learning
por: Fuentes-Vicente, Laura, et al.
Publicado: (2026)
por: Fuentes-Vicente, Laura, et al.
Publicado: (2026)
On Instability of Minimax Optimal Optimism-Based Bandit Algorithms
por: Praharaj, Samya, et al.
Publicado: (2025)
por: Praharaj, Samya, et al.
Publicado: (2025)
Asymptotically Optimal Sequential Testing with Markovian Data
por: Sethi, Alhad, et al.
Publicado: (2026)
por: Sethi, Alhad, et al.
Publicado: (2026)
Minimax Rate-Optimal Algorithms for High-Dimensional Stochastic Linear Bandits
por: Liu, Jingyu, et al.
Publicado: (2025)
por: Liu, Jingyu, et al.
Publicado: (2025)
Towards Efficient and Optimal Covariance-Adaptive Algorithms for Combinatorial Semi-Bandits
por: Zhou, Julien, et al.
Publicado: (2024)
por: Zhou, Julien, et al.
Publicado: (2024)
Statistical Complexity and Optimal Algorithms for Non-linear Ridge Bandits
por: Rajaraman, Nived, et al.
Publicado: (2023)
por: Rajaraman, Nived, et al.
Publicado: (2023)
Meta-Learning with Generalized Ridge Regression: High-dimensional Asymptotics, Optimality and Hyper-covariance Estimation
por: Jin, Yanhao, et al.
Publicado: (2024)
por: Jin, Yanhao, et al.
Publicado: (2024)
Multitask Learning and Bandits via Robust Statistics
por: Xu, Kan, et al.
Publicado: (2021)
por: Xu, Kan, et al.
Publicado: (2021)
Source-Optimal Training is Transfer-Suboptimal
por: Hedges, C. Evans
Publicado: (2025)
por: Hedges, C. Evans
Publicado: (2025)
Adaptive Lasso, Transfer Lasso, and Beyond: An Asymptotic Perspective
por: Takada, Masaaki, et al.
Publicado: (2023)
por: Takada, Masaaki, et al.
Publicado: (2023)
A Diffusion Analysis of Policy Gradient for Stochastic Bandits
por: Lattimore, Tor
Publicado: (2026)
por: Lattimore, Tor
Publicado: (2026)
Optimal Transport under Group Fairness Constraints
por: Bleistein, Linus, et al.
Publicado: (2026)
por: Bleistein, Linus, et al.
Publicado: (2026)
Multi-Armed Bandits With Machine Learning-Generated Surrogate Rewards
por: Ji, Wenlong, et al.
Publicado: (2025)
por: Ji, Wenlong, et al.
Publicado: (2025)
Towards a Sharp Analysis of Offline Policy Learning for $f$-Divergence-Regularized Contextual Bandits
por: Zhao, Qingyue, et al.
Publicado: (2025)
por: Zhao, Qingyue, et al.
Publicado: (2025)
Near-Optimal Regret for KL-Regularized Multi-Armed Bandits
por: Ji, Kaixuan, et al.
Publicado: (2026)
por: Ji, Kaixuan, et al.
Publicado: (2026)
Regret Distribution in Stochastic Bandits: Optimal Trade-off between Expectation and Tail Risk
por: Simchi-Levi, David, et al.
Publicado: (2023)
por: Simchi-Levi, David, et al.
Publicado: (2023)
Aligning Embeddings and Geometric Random Graphs: Informational Results and Computational Approaches for the Procrustes-Wasserstein Problem
por: Even, Mathieu, et al.
Publicado: (2024)
por: Even, Mathieu, et al.
Publicado: (2024)
Higher-Order Regularization Learning on Hypergraphs
por: Weihs, Adrien, et al.
Publicado: (2025)
por: Weihs, Adrien, et al.
Publicado: (2025)
Analysis of Semi-Supervised Learning on Hypergraphs
por: Weihs, Adrien, et al.
Publicado: (2025)
por: Weihs, Adrien, et al.
Publicado: (2025)
Optimal Regret of Bernoulli Bandits under Global Differential Privacy
por: Azize, Achraf, et al.
Publicado: (2025)
por: Azize, Achraf, et al.
Publicado: (2025)
The Fragility of Optimized Bandit Algorithms
por: Fan, Lin, et al.
Publicado: (2021)
por: Fan, Lin, et al.
Publicado: (2021)
Batched Nonparametric Contextual Bandits
por: Jiang, Rong, et al.
Publicado: (2024)
por: Jiang, Rong, et al.
Publicado: (2024)
Laplace Transform Based Low-Complexity Learning of Continuous Markov Semigroups
por: Kostic, Vladimir R., et al.
Publicado: (2024)
por: Kostic, Vladimir R., et al.
Publicado: (2024)
Doubly Robust Interval Estimation for Optimal Policy Evaluation in Online Learning
por: Shen, Ye, et al.
Publicado: (2021)
por: Shen, Ye, et al.
Publicado: (2021)
Ejemplares similares
-
Leveraging priors on distribution functions for multi-arm bandits
por: Vashishtha, Sumit, et al.
Publicado: (2025) -
How Hard is it to Confuse a World Model?
por: Radji, Waris, et al.
Publicado: (2025) -
The regret lower bound for communicating Markov Decision Processes
por: Boone, Victor, et al.
Publicado: (2025) -
The Confusing Instance Principle for Online Linear Quadratic Control
por: Radji, Waris, et al.
Publicado: (2025) -
Hierarchical Subspaces of Policies for Continual Offline Reinforcement Learning
por: Kobanda, Anthony, et al.
Publicado: (2024)