Cover Image

On Lai's Upper Confidence Bound in Multi-Armed Bandits

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Ren, Huachen, Zhang, Cun-Hui
Format:	Preprint
Published:	2024
Subjects:	Machine Learning Statistics Theory 62L05, 62L10 (Primary) 68T05 (Secondary)
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Kernel $ε$-Greedy for Multi-Armed Bandits with Covariates
by: Arya, Sakshi, et al.
Published: (2023)

Validity and efficiency of the conformal CUSUM procedure
by: Vovk, Vladimir, et al.
Published: (2024)

Conformal e-testing
by: Vovk, Vladimir, et al.
Published: (2020)

Round Robin Active Sequential Change Detection for Dependent Multi-Channel Data
by: Chaudhuri, Anamitra, et al.
Published: (2024)

Batched Single-Index Global Multi-Armed Bandits with Covariates
by: Arya, Sakshi, et al.
Published: (2025)

An exploration of sequential Bayesian variable selection -- A comment on García-Donato et al. (2025). "Model uncertainty and missing data: An objective Bayesian perspective"
by: Arnold, Sebastian, et al.
Published: (2025)

Kernel Single-Index Bandits: Estimation, Inference, and Learning
by: Arya, Sakshi, et al.
Published: (2026)

An Efficient Adaptive Sequential Procedure for Simple Hypotheses with Expression for Finite Number of Applications of Less Effective Treatment
by: Kundu, Sampurna, et al.
Published: (2025)

Differentially Private Fisher Randomization Tests for Binary Outcomes
by: Sun, Qingyang, et al.
Published: (2025)

Revisiting Step-Size Assumptions in Stochastic Approximation
by: Lauand, Caio Kalil, et al.
Published: (2024)

The case for and against fixed step-size: Stochastic approximation algorithms in optimization and machine learning
by: Lauand, Caio Kalil, et al.
Published: (2023)

A studentized permutation test in group sequential designs
by: Xu, Long-Hao, et al.
Published: (2024)

Reinforcement Learning with Action-Triggered Observations
by: Ryabchenko, Alexander, et al.
Published: (2025)

seMCD: Sequentially implemented Monte Carlo depth computation with statistical guarantees
by: Gnettner, Felix, et al.
Published: (2025)

Inference with Sequential Monte-Carlo Computation of $p$-values: Fast and Valid Approaches
by: Stoepker, Ivo V., et al.
Published: (2024)

Causality as the Statistical Conscience of Artificial Intelligence: From Pearl's Ladder to Trustworthy Machines
by: Fokoué, Ernest
Published: (2026)

Principles of Statistical Inference in Online Problems
by: Leung, Man Fung, et al.
Published: (2022)

Sequential change-point detection for generalized Ornstein-Uhlenbeck processes
by: Lyu, Yunhong, et al.
Published: (2025)

OML-AD: Online Machine Learning for Anomaly Detection in Time Series Data
by: Wette, Sebastian, et al.
Published: (2024)

Asymptotic uniqueness in long-term prediction
by: Vovk, Vladimir
Published: (2023)

Sequential Monte Carlo Bandits
by: Urteaga, Iñigo, et al.
Published: (2018)

Batched Nonparametric Bandits via k-Nearest Neighbor UCB
by: Arya, Sakshi
Published: (2025)

Generalized Functional ANOVA in Closed-Form: A Unified View of Additive Explanations
by: Ferrere, Baptiste, et al.
Published: (2026)

High-Dimensional Change-Point Detection via Angular Kernel Statistics
by: Choudhury, Jyotishka Ray, et al.
Published: (2026)

A new and flexible class of sharp asymptotic time-uniform confidence sequences
by: Gnettner, Felix, et al.
Published: (2025)

Computing conservative probabilities of rare events with surrogates
by: Bousquet, Nicolas
Published: (2024)

Two approaches to multiple canonical correlation analysis for repeated measures data
by: Górecki, Tomasz, et al.
Published: (2025)

The ODE Method for Asymptotic Statistics in Stochastic Approximation and Reinforcement Learning
by: Borkar, Vivek, et al.
Published: (2021)

Data-driven Error Estimation: Excess Risk Bounds without Class Complexity as Input
by: Krishnamurthy, Sanath Kumar, et al.
Published: (2024)

MSTN: A Lightweight and Fast Model for General TimeSeries Analysis
by: Shevtekar, Sumit S, et al.
Published: (2025)

FedSTaS: Client Stratification and Client Level Sampling for Efficient Federated Learning
by: Slessor, Jordan, et al.
Published: (2024)

Online Learning of Pure States is as Hard as Mixed States
by: Meyer, Maxime, et al.
Published: (2025)

Conformal e-prediction in the presence of confounding
by: Vovk, Vladimir, et al.
Published: (2026)

Universality of conformal prediction under the assumption of randomness
by: Vovk, Vladimir
Published: (2025)

Semi-supervised linear regression with missing covariates
by: Risebrow, Benedict M., et al.
Published: (2026)

ALMAB-DC: Active Learning, Multi-Armed Bandits, and Distributed Computing for Sequential Experimental Design and Black-Box Optimization
by: Hui-Mean, Foo, et al.
Published: (2026)

Non-Steepness and Maximum Likelihood Estimation Properties of the Truncated Multivariate Normal Distributions
by: Levine, Michael, et al.
Published: (2023)

Quantifying and testing dependence to categorical variables
by: Hörmann, Siegfried, et al.
Published: (2025)

When Your Model Stops Working: Anytime-Valid Calibration Monitoring
by: Farran, Tristan
Published: (2026)

Stratified Permutational Berry--Esseen Bounds and Their Applications to Statistics
by: Tian, Pengfei, et al.
Published: (2025)