activity
20182026
most citedInfinite Arms Bandit: Optimality via Confidence Bounds

3 citations · 6 across the 6 of their papers we have counts for

collaborators

6 papers

stat.ME2026

Structural Change Detection in High-Dimensional Transformed Factor Models via Canonical Correlation Analysis

Lei Jia, Shouri Hu, Zhaoxing Gao

This paper develops a canonical-correlation-based method for detecting structural changes in high-dimensional transformed factor models. The proposed approach exploits the low-rank…

stat.ML2024

An Adaptive Dimension Reduction Estimation Method for High-dimensional Bayesian Optimization

Shouri Hu, Jiawei Li, Zhibo Cai

Bayesian optimization (BO) has shown impressive results in a variety of applications within low-to-moderate dimensional Euclidean spaces. However, extending BO to high-dimensional…

cs.LG2022★ 2 cited

Adjusted Expected Improvement for Cumulative Regret Minimization in Noisy Bayesian Optimization

Shouri Hu, Haowei Wang, Zhongxiang Dai +2

The expected improvement (EI) is one of the most popular acquisition functions for Bayesian optimization (BO) and has demonstrated good empirical performances in many applications…

math.ST2021

Likelihood Scores for Sparse Signal and Change-Point Detection

Shouri Hu, Jingyan Huang, Hao Chen +1

We consider here the identification of change-points on large-scale data streams. The objective is to find the most efficient way of combining information across data stream so tha…

math.ST2019★ 1 cited

Optimal UCB Adjustments for Large Arm Sizes

Hock Peng Chan, Shouri Hu

The regret lower bound of Lai and Robbins (1985), the gold standard for checking optimality of bandit algorithms, considers arm size fixed as sample size goes to infinity. We show…

stat.ML2018★ 3 cited

Infinite Arms Bandit: Optimality via Confidence Bounds

Hock Peng Chan, Shouri Hu

Berry et al. (1997) initiated the development of the infinite arms bandit problem. They derived a regret lower bound of all allocation strategies for Bernoulli rewards with uniform…