Cover Image

Last-Iterate Analyses of FTRL with the 1/2-Tsallis Entropy in Stochastic Bandits

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Zhan, Jingxin, Han, Yuze, Zhang, Zhihua
Format:	Preprint
Published:	2025
Subjects:	Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

A Regularized Online Newton Method for Stochastic Convex Bandits with Linear Vanishing Noise
by: Zhan, Jingxin, et al.
Published: (2025)

Follow-the-Perturbed-Leader Approaches Best-of-Both-Worlds for the m-Set Semi-Bandit Problems
by: Zhan, Jingxin, et al.
Published: (2025)

Finite-Time Decoupled Convergence in Nonlinear Two-Time-Scale Stochastic Approximation
by: Han, Yuze, et al.
Published: (2024)

Uniform Last-Iterate Guarantee for Bandits and Reinforcement Learning
by: Liu, Junyan, et al.
Published: (2024)

Decoupled Functional Central Limit Theorems for Two-Time-Scale Stochastic Approximation
by: Han, Yuze, et al.
Published: (2024)

Conformal-Style Quantile Analyses for Stochastic Bandits
by: Du, Chengyu, et al.
Published: (2026)

Near-Optimal Last-Iterate Convergence for Zero-Sum Games with Bandit Feedback and Opponent Actions
by: Hait, Soumita, et al.
Published: (2026)

Optimistic Online Non-stochastic Control via FTRL
by: Mhaisen, Naram, et al.
Published: (2024)

Convergence Rate of the Last Iterate of Stochastic Proximal Algorithms
by: Vaidyan, Kevin Kurian Thomas, et al.
Published: (2026)

Revisiting the Last-Iterate Convergence of Stochastic Gradient Methods
by: Liu, Zijian, et al.
Published: (2023)

The Harder Path: Last Iterate Convergence for Uncoupled Learning in Zero-Sum Games with Bandit Feedback
by: Fiegel, Côme, et al.
Published: (2026)

LC-Tsallis-INF: Generalized Best-of-Both-Worlds Linear Contextual Bandits
by: Kato, Masahiro, et al.
Published: (2024)

Understanding Adam Optimizer via Online Learning of Updates: Adam is FTRL in Disguise
by: Ahn, Kwangjun, et al.
Published: (2024)

Exploratory Utility Maximization Problem with Tsallis Entropy
by: Ziyi, Chen, et al.
Published: (2025)

Bayesian Bandit Algorithms with Approximate Inference in Stochastic Linear Bandits
by: Huang, Ziyi, et al.
Published: (2024)

Online Learning Quantum States with the Logarithmic Loss via VB-FTRL
by: Tseng, Wei-Fu, et al.
Published: (2023)

Continuous-time q-Learning for Jump-Diffusion Models under Tsallis Entropy
by: Bo, Lijun, et al.
Published: (2024)

Last-Iterate Convergence of Payoff-Based Independent Learning in Zero-Sum Stochastic Games
by: Chen, Zaiwei, et al.
Published: (2024)

Entropy-based Training Methods for Scalable Neural Implicit Sampler
by: Luo, Weijian, et al.
Published: (2023)

Stochastic Graph Bandit Learning with Side-Observations
by: Gong, Xueping, et al.
Published: (2023)

Stochastic Bandits for Egalitarian Assignment
by: Lim, Eugene, et al.
Published: (2024)

Efficient Clustering in Stochastic Bandits
by: Chandran, G Dhinesh, et al.
Published: (2026)

Stochastic Gradient Succeeds for Bandits
by: Mei, Jincheng, et al.
Published: (2024)

SOREL: A Stochastic Algorithm for Spectral Risks Minimization
by: Ge, Yuze, et al.
Published: (2024)

A Simple and Adaptive Learning Rate for FTRL in Online Learning with Minimax Regret of $Θ(T^{2/3})$ and its Application to Best-of-Both-Worlds
by: Tsuchiya, Taira, et al.
Published: (2024)

Tsallis Entropy Regularization for Linearly Solvable MDP and Linear Quadratic Regulator
by: Hashizume, Yota, et al.
Published: (2024)

Are Stochastic Multi-objective Bandits Harder than Single-objective Bandits?
by: Guan, Changkun, et al.
Published: (2026)

Lipschitz Bandits with Stochastic Delayed Feedback
by: Liu, Zhongxuan, et al.
Published: (2025)

Batched Stochastic Bandit for Nondegenerate Functions
by: Liu, Yu, et al.
Published: (2024)

Stochastic Bandits Robust to Adversarial Attacks
by: Wang, Xuchuang, et al.
Published: (2024)

On the Last-Iterate Convergence of Shuffling Gradient Methods
by: Liu, Zijian, et al.
Published: (2024)

Augmented Lagrangian Method for Last-Iterate Convergence for Constrained MDPs
by: Lu, Michael, et al.
Published: (2026)

Convergence Rate for the Last Iterate of Stochastic Gradient Descent Schemes
by: Hudiani, Marcel
Published: (2025)

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

Stochastic Matching Bandits with Rare Optimization Updates
by: Kim, Jung-hun, et al.
Published: (2025)

Active Learning for Stochastic Contextual Linear Bandits
by: Brunskill, Emma, et al.
Published: (2026)

Biased Dueling Bandits with Stochastic Delayed Feedback
by: Yi, Bongsoo, et al.
Published: (2024)

Best Arm Identification for Stochastic Rising Bandits
by: Mussi, Marco, et al.
Published: (2023)

Offline Local Search for Online Stochastic Bandits
by: Benadè, Gerdus, et al.
Published: (2026)

Forget by Uncertainty: Orthogonal Entropy Unlearning for Quantized Neural Networks
by: Zhang, Tian, et al.
Published: (2026)