Search Results for author: L. Jeff Hong

Found 8 papers, 1 papers with code

AlphaRank: An Artificial Intelligence Approach for Ranking and Selection Problems

no code implementations1 Feb 2024 Ruihan Zhou, L. Jeff Hong, Yijie Peng

We introduce AlphaRank, an artificial intelligence approach to address the fixed-budget ranking and selection (R&S) problems.

Learning to Simulate: Generative Metamodeling via Quantile Regression

no code implementations29 Nov 2023 L. Jeff Hong, Yanxi Hou, Qingkai Zhang, Xiaowei Zhang

In this paper, we propose a new metamodeling concept, called generative metamodeling, which aims to construct a "fast simulator of the simulator".

Decision Making regression

Monte-Carlo Estimation of CoVaR

no code implementations12 Oct 2022 Weihuan Huang, Nifei Lin, L. Jeff Hong

We then develop an importance-sampling inspired estimator under the delta-gamma approximations to the portfolio losses, and we show that the rate of convergence of the estimator is $n^{-1/2}$.

Large-Scale Inventory Optimization: A Recurrent-Neural-Networks-Inspired Simulation Approach

no code implementations15 Jan 2022 Tan Wan, L. Jeff Hong

Many large-scale production networks include thousands types of final products and tens to hundreds thousands types of raw materials and intermediate products.

Management

Dimension Reduction in Contextual Online Learning via Nonparametric Variable Selection

no code implementations17 Sep 2020 Wenhao Li, Ningyuan Chen, L. Jeff Hong

Our algorithm achieves the regret $\tilde{O}(T^{(d_x^*+d_y+1)/(d_x^*+d_y+2)})$, where $d_x^*$ is the effective covariate dimension.

Dimensionality Reduction Variable Selection

Review on Ranking and Selection: A New Perspective

1 code implementation1 Aug 2020 L. Jeff Hong, Weiwei Fan, Jun Luo

In this paper, we briefly review the development of ranking-and-selection (R&S) in the past 70 years, especially the theoretical achievements and practical applications in the last 20 years.

Optimization and Control Methodology

A Dimension-free Algorithm for Contextual Continuum-armed Bandits

no code implementations15 Jul 2019 Wenhao Li, Ningyuan Chen, L. Jeff Hong

The literature has shown that for Lipschitz-continuous functions, the optimal regret is $\tilde{O}(T^{\frac{d_x+d_y+1}{d_x+d_y+2}})$, where $d_x$ and $d_y$ are the dimensions of contexts and arms, and thus suffers from the curse of dimensionality.

Ranking and Selection with Covariates for Personalized Decision Making

no code implementations7 Oct 2017 Haihui Shen, L. Jeff Hong, Xiaowei Zhang

The goal of ranking and selection with covariates (R&S-C) is to use simulation samples to obtain a selection policy that specifies the best alternative with certain statistical guarantee for subsequent individuals upon observing their covariates.

Decision Making Experimental Design

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