Search Results - "Mak, Simon"

Energy balancing of covariate distributions by Huling, Jared D., Mak, Simon

“…Bias in causal comparisons has a correspondence with distributional imbalance of covariates between treatment groups. Weighting strategies such as inverse…”
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TSEC: A Framework for Online Experimentation under Experimental Constraints by Mak, Simon, Zhao, Yuanshuo, Hoang, Lavonne, Wu, C. F. Jeff

“…Thompson sampling is a popular algorithm for tackling multi-armed bandit problems, and has been applied in a wide range of applications, from website design to…”
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Warfarin control in Hong Kong clinical practice: a single-centre observational study by Lam, Amy S M, Lee, Isis M H, Mak, Simon K S, Yan, Bryan P Y, Lee, Vivian W Y

“…INTRODUCTIONTime in therapeutic range (TTR) assesses the safety and effectiveness of warfarin therapy using the international normalised ratio. This study…”
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Minimax and Minimax Projection Designs Using Clustering by Mak, Simon, Joseph, V. Roshan

“…Minimax designs provide a uniform coverage of a design space X ⊆ ℝp by minimizing the maximum distance from any point in this space to its nearest design…”
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SUPPORT POINTS by Mak, Simon, Joseph, V. Roshan

“…This paper introduces a new way to compact a continuous probability distribution F into a set of representative points called support points. These points are…”
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Bayesian Uncertainty Quantification for Low-Rank Matrix Completion by Yuchi, Henry Shaowu, Mak, Simon, Xie, Yao

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The Impact of Business Investment on Capability Exploitation and Organizational Control in International Strategic Alliances by Yan, Yanni, Ding, Daniel, Mak, Simon

“…The purpose of this paper is to address how a firm that adopts various organizational controls can moderate the relationship between business investment and…”
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A Hierarchical Expected Improvement Method for Bayesian Optimization by Chen, Zhehui, Mak, Simon, Wu, C. F. Jeff

“…The Expected Improvement (EI) method, proposed by Jones, Schonlau, andWelch, is a widely used Bayesian optimization method, which makes use of a fitted…”
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PERCEPT: A New Online Change-Point Detection Method using Topological Data Analysis by Zheng, Xiaojun, Mak, Simon, Xie, Liyan, Xie, Yao

“…Topological data analysis (TDA) provides a set of data analysis tools for extracting embedded topological structures from complex high-dimensional datasets. In…”
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Sequential Change-Point Detection for Mutually Exciting Point Processes by Wang, Haoyun, Xie, Liyan, Xie, Yao, Cuozzo, Alex, Mak, Simon

“…We present a new CUSUM procedure for sequential change-point detection in self- and mutually-exciting point processes (specifically, Hawkes networks) using…”
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An Efficient Surrogate Model for Emulation and Physics Extraction of Large Eddy Simulations by Mak, Simon, Sung, Chih-Li, Wang, Xingjian, Yeh, Shiang-Ting, Chang, Yu-Hung, Joseph, V. Roshan, Yang, Vigor, Wu, C. F. Jeff

“…In the quest for advanced propulsion and power-generation systems, high-fidelity simulations are too computationally expensive to survey the desired design…”
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Supervised compression of big data by Joseph, V. Roshan, Mak, Simon

“…The phenomenon of big data has become ubiquitous in nearly all disciplines, from science to engineering. A key challenge is the use of such data for fitting…”
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Population Quasi-Monte Carlo by Huang, Chaofan, Joseph, V. Roshan, Mak, Simon

“…Monte Carlo methods are widely used for approximating complicated, multidimensional integrals for Bayesian inference. Population Monte Carlo (PMC) is an…”
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Function-on-Function Kriging, With Applications to Three-Dimensional Printing of Aortic Tissues by Chen, Jialei, Mak, Simon, Joseph, V. Roshan, Zhang, Chuck

“…Three-dimensional printed medical prototypes, which use synthetic metamaterials to mimic biological tissue, are becoming increasingly important in urgent…”
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Maximum Entropy Low-Rank Matrix Recovery by Mak, Simon, Yao Xie

“…We propose a novel, information-theoretic method, called MaxEnt, for efficient data acquisition for low-rank matrix recovery. This proposed method has…”
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eRPCA: Robust Principal Component Analysis for Exponential Family Distributions by Zheng, Xiaojun, Mak, Simon, Xie, Liyan, Xie, Yao

“…Robust principal component analysis (RPCA) is a widely used method for recovering low‐rank structure from data matrices corrupted by significant and sparse…”
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e RPCA : Robust Principal Component Analysis for Exponential Family Distributions by Zheng, Xiaojun, Mak, Simon, Xie, Liyan, Xie, Yao

“…Abstract Robust principal component analysis (RPCA) is a widely used method for recovering low‐rank structure from data matrices corrupted by significant and…”
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A Graphical Multi-Fidelity Gaussian Process Model, with Application to Emulation of Heavy-Ion Collisions by Ji, Yi, Mak, Simon, Soeder, Derek, Paquet, J-F, Bass, Steffen A.

“…With advances in scientific computing and mathematical modeling, complex scientific phenomena such as galaxy formations and rocket propulsion can now be…”
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Analysis-of-Marginal-Tail-Means (ATM): A Robust Method for Discrete Black-Box Optimization by Mak, Simon, Jeff Wu, C. F.

“…We present a new method, called analysis-of-marginal-tail-means (ATM), for effective robust optimization of discrete black-box problems. ATM has important…”
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cmenet : A New Method for Bi-Level Variable Selection of Conditional Main Effects by Mak, Simon, Wu, C. F. Jeff

“…This article introduces a novel method for selecting main effects and a set of reparameterized effects called conditional main effects (CMEs), which capture…”
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