Copertina

Near-Optimal Regret for Efficient Stochastic Combinatorial Semi-Bandits

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Ye, Zichun, Wang, Runqi, Liu, Xutong, Li, Shuai
Natura:	Preprint
Pubblicazione:	2025
Soggetti:	Machine Learning Data Structures and Algorithms
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Unlearning Offline Stochastic Multi-Armed Bandits
di: Ye, Zichun, et al.
Pubblicazione: (2026)

Understanding Memory-Regret Trade-Off for Streaming Stochastic Multi-Armed Bandits
di: He, Yuchen, et al.
Pubblicazione: (2024)

Tight Gap-Dependent Memory-Regret Trade-Off for Single-Pass Streaming Stochastic Multi-Armed Bandits
di: Ye, Zichun, et al.
Pubblicazione: (2025)

Stochastic Multi-Objective Multi-Armed Bandits: Regret Definition and Algorithm
di: Davoodi, Mansoor, et al.
Pubblicazione: (2025)

Semi-Bandit Learning for Monotone Stochastic Optimization
di: Agarwal, Arpit, et al.
Pubblicazione: (2023)

Adversarial Attacks on Combinatorial Multi-Armed Bandits
di: Balasubramanian, Rishab, et al.
Pubblicazione: (2023)

No-Regret M${}^{\natural}$-Concave Function Maximization: Stochastic Bandit Algorithms and Hardness of Adversarial Full-Information Setting
di: Oki, Taihei, et al.
Pubblicazione: (2024)

Nearly Tight Bounds for Exploration in Streaming Multi-armed Bandits with Known Optimality Gap
di: Karpov, Nikolai, et al.
Pubblicazione: (2025)

Stochastic $k$-Submodular Bandits with Full Bandit Feedback
di: Nie, Guanyu, et al.
Pubblicazione: (2024)

Near-optimal Swap Regret Minimization for Convex Losses
di: Hu, Lunjia, et al.
Pubblicazione: (2026)

Stochastic Bandits with ReLU Neural Networks
di: Xu, Kan, et al.
Pubblicazione: (2024)

Towards Optimal Differentially Private Regret Bounds in Linear MDPs
di: Sahu, Sharan
Pubblicazione: (2025)

Online Algorithms for Repeated Optimal Stopping: Balancing Baseline Guarantees and Regret
di: Harada, Tsubasa, et al.
Pubblicazione: (2025)

Nearly-tight Approximation Guarantees for the Improving Multi-Armed Bandits Problem
di: Blum, Avrim, et al.
Pubblicazione: (2024)

Quantum Algorithms for Bandits with Knapsacks with Improved Regret and Time Complexities
di: Su, Yuexin, et al.
Pubblicazione: (2025)

Efficient, Low-Regret, Online Reinforcement Learning for Linear MDPs
di: John, Philips George, et al.
Pubblicazione: (2024)

Near-Optimal Algorithms for Omniprediction
di: Okoroafor, Princewill, et al.
Pubblicazione: (2025)

The Best Arm Evades: Near-optimal Multi-pass Streaming Lower Bounds for Pure Exploration in Multi-armed Bandits
di: Assadi, Sepehr, et al.
Pubblicazione: (2023)

Submodular Maximization subject to a Knapsack Constraint: Combinatorial Algorithms with Near-optimal Adaptive Complexity
di: Amanatidis, Georgios, et al.
Pubblicazione: (2021)

Stochastic Submodular Bandits with Delayed Composite Anonymous Bandit Feedback
di: Pedramfar, Mohammad, et al.
Pubblicazione: (2023)

A Single-Sample Polylogarithmic Regret Bound for Nonstationary Online Linear Programming
di: Xu, Haoran, et al.
Pubblicazione: (2026)

Optimal Scalarizations for Sublinear Hypervolume Regret
di: Zhang, Qiuyi
Pubblicazione: (2023)

Private Stochastic Convex Optimization with Heavy Tails: Near-Optimality from Simple Reductions
di: Asi, Hilal, et al.
Pubblicazione: (2024)

Improved Regret in Stochastic Decision-Theoretic Online Learning under Differential Privacy
di: Wu, Ruihan, et al.
Pubblicazione: (2025)

Ads that Stick: Near-Optimal Ad Optimization through Psychological Behavior Models
di: Darmasubramanian, Kailash Gopal, et al.
Pubblicazione: (2025)

Approximation Algorithms for Combinatorial Optimization with Predictions
di: Antoniadis, Antonios, et al.
Pubblicazione: (2024)

Linear Submodular Maximization with Bandit Feedback
di: Chen, Wenjing, et al.
Pubblicazione: (2024)

High-dimensional Linear Bandits with Knapsacks
di: Ma, Wanteng, et al.
Pubblicazione: (2023)

A Model for Combinatorial Dictionary Learning and Inference
di: Blum, Avrim, et al.
Pubblicazione: (2024)

MNL-Bandit with Knapsacks: a near-optimal algorithm
di: Aznag, Abdellah, et al.
Pubblicazione: (2021)

From Average Sensitivity to Small-Loss Regret Bounds under Random-Order Model
di: Sakaue, Shinsaku, et al.
Pubblicazione: (2026)

Differential Private Stochastic Optimization with Heavy-tailed Data: Towards Optimal Rates
di: Zhao, Puning, et al.
Pubblicazione: (2024)

$O(\sqrt{T})$ Static Regret and Instance Dependent Constraint Violation for Constrained Online Convex Optimization
di: Vaze, Rahul, et al.
Pubblicazione: (2025)

Private Edge Density Estimation for Random Graphs: Optimal, Efficient and Robust
di: Chen, Hongjie, et al.
Pubblicazione: (2024)

Near-optimal Active Regression of Single-Index Models
di: Li, Yi, et al.
Pubblicazione: (2025)

Combinatorial optimization of the coefficient of determination
di: Harary, Marc
Pubblicazione: (2024)

Greedy Algorithm for Structured Bandits: A Sharp Characterization of Asymptotic Success / Failure
di: Slivkins, Aleksandrs, et al.
Pubblicazione: (2025)

Finite and Corruption-Robust Regret Bounds in Online Inverse Linear Optimization under M-Convex Action Sets
di: Oki, Taihei, et al.
Pubblicazione: (2026)

Nearly-Linear Time Private Hypothesis Selection with the Optimal Approximation Factor
di: Aliakbarpour, Maryam, et al.
Pubblicazione: (2025)

Near-Optimal Sparsifiers for Stochastic Knapsack and Assignment Problems
di: Dughmi, Shaddin, et al.
Pubblicazione: (2025)