Cover Image

Box Thirding: Anytime Best Arm Identification under Insufficient Sampling

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Hwang, Seohwa, Park, Junyong
Format:	Preprint
Published:	2026
Subjects:	Machine Learning 62L05
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

FDR Control via Neural Networks under Covariate-Dependent Symmetric Nulls
by: Kim, Taehyoung, et al.
Published: (2025)

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

Function-on-Function Bayesian Optimization
by: Huang, Jingru, et al.
Published: (2025)

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)

Graph Based, Adaptive, Multi Arm, Multiple Endpoint, Two Stage Design
by: Mehta, Cyrus, et al.
Published: (2025)

On Lai's Upper Confidence Bound in Multi-Armed Bandits
by: Ren, Huachen, et al.
Published: (2024)

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

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

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

Improving the Convergence Rates of Forward Gradient Descent with Repeated Sampling
by: Dexheimer, Niklas, et al.
Published: (2024)

PROXIMA: A Reliability Scoring Framework for Proxy Metrics in Online Controlled Experiments
by: Amudala, Avinash
Published: (2026)

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

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

Bayesian Sequential Optimal Experimental Design for Nonlinear Models Using Policy Gradient Reinforcement Learning
by: Shen, Wanggang, et al.
Published: (2021)

Accelerating Particle-based Energetic Variational Inference
by: Bao, Xuelian, et al.
Published: (2025)

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

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

Local regression on path spaces with signature metrics
by: Bayer, Christian, et al.
Published: (2025)

Extensions of the regret-minimization algorithm for optimal design
by: Chen, Youguang, et al.
Published: (2025)

Variational Sequential Optimal Experimental Design using Reinforcement Learning
by: Shen, Wanggang, et al.
Published: (2023)

A General Framework for Off-Policy Learning with Partially-Observed Reward
by: Takehi, Rikiya, et al.
Published: (2025)

Truncated Kernel Stochastic Gradient Descent with General Losses and Spherical Radial Basis Functions
by: Bai, Jinhui, et al.
Published: (2025)

Mode Estimation with Partial Feedback
by: Arnal, Charles, et al.
Published: (2024)

Are causal effect estimations enough for optimal recommendations under multitreatment scenarios?
by: Alfonso-Sánchez, Sherly, et al.
Published: (2024)

Distributed sequential federated learning
by: Wang, Z. F., et al.
Published: (2023)

Grouping predictors via network-wide metrics
by: Park, Brandon Woosuk, et al.
Published: (2024)

Sampling Audit Evidence Using a Naive Bayes Classifier
by: Sheu, Guang-Yih, et al.
Published: (2024)

Online Regularized Learning Algorithms in RKHS with $β$- and $ϕ$-Mixing Sequences
by: Roy, Priyanka, et al.
Published: (2025)

Variance-Reduced Manifold Sampling via Polynomial-Maximization Density Estimation
by: Zabolotnii, Serhii
Published: (2026)

When Does Dynamic Preconditioning Preserve the Polyak-Ruppert CLT? A Stabilization Threshold
by: An, Sunyoung, et al.
Published: (2026)

Statistical inference for Linear Stochastic Approximation with Markovian Noise
by: Samsonov, Sergey, et al.
Published: (2025)

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

AEGIS: An Operational Infrastructure for Post-Market Governance of Adaptive Medical AI Under US and EU Regulations
by: Afdideh, Fardin, et al.
Published: (2026)

An Iterative Bayesian Robbins--Monro Sequence
by: Liu, Siwei, et al.
Published: (2025)

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)

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

mfEGRA: Multifidelity Efficient Global Reliability Analysis through Active Learning for Failure Boundary Location
by: Chaudhuri, Anirban, et al.
Published: (2019)

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

PCA-Guided Quantile Sampling: Preserving Data Structure in Large-Scale Subsampling
by: Hui-Mean, Foo, et al.
Published: (2025)

Gaussian Approximation and Multiplier Bootstrap for Polyak-Ruppert Averaged Linear Stochastic Approximation with Applications to TD Learning
by: Samsonov, Sergey, et al.
Published: (2024)