1 code implementation • 30 Oct 2023 • Hengrui Luo, Jisu Kim, Alice Patania, Mikael Vejdemo-Johansson
Topology can extract the structural information in a dataset efficiently.
no code implementations • 18 Sep 2023 • Marcus M. Noack, Hengrui Luo, Mark D. Risser
The Gaussian process (GP) is a popular statistical technique for stochastic function approximation and uncertainty quantification from data.
no code implementations • 30 Aug 2023 • Younghyun Cho, James W. Demmel, Michał Dereziński, Haoyun Li, Hengrui Luo, Michael W. Mahoney, Riley J. Murray
Algorithms from Randomized Numerical Linear Algebra (RandNLA) are known to be effective in handling high-dimensional computational problems, providing high-quality empirical performance as well as strong probabilistic guarantees.
no code implementations • 1 Jun 2023 • Hengrui Luo, Matthew T. Pratola
In this paper we develop the randomized Sharded Bayesian Additive Regression Trees (SBT) model.
no code implementations • 1 Jun 2023 • Yin-Ting Liao, Hengrui Luo, Anna Ma
We introduce an efficient and robust auto-tuning framework for hyperparameter selection in dimension reduction (DR) algorithms, focusing on large-scale datasets and arbitrary performance metrics.
no code implementations • 20 May 2023 • Sam Hawke, Hengrui Luo, Didong Li
Supervised dimension reduction (SDR) has been a topic of growing interest in data science, as it enables the reduction of high-dimensional covariates while preserving the functional relation with certain response variables of interest.
no code implementations • 18 Jun 2022 • Hengrui Luo, Justin D. Strait
The modeling and uncertainty quantification of closed curves is an important problem in the field of shape analysis, and can have significant ramifications for subsequent statistical tasks.
1 code implementation • 3 Jun 2022 • Hengrui Luo, Younghyun Cho, James W. Demmel, Xiaoye S. Li, Yang Liu
This paper presents a new type of hybrid model for Bayesian optimization (BO) adept at managing mixed variables, encompassing both quantitative (continuous and integer) and qualitative (categorical) types.
1 code implementation • 23 Apr 2022 • Hengrui Luo, Jeremy E. Purvis, Didong Li
Modern datasets often exhibit high dimensionality, yet the data reside in low-dimensional manifolds that can reveal underlying geometric structures critical for data analysis.
1 code implementation • 15 Sep 2021 • Hengrui Luo, James W. Demmel, Younghyun Cho, Xiaoye S. Li, Yang Liu
By using this surrogate model, we want to capture the non-smoothness of the black-box function.
1 code implementation • 8 Sep 2020 • Leland Wilkinson, Hengrui Luo
This selection is designed to preserve relative distances as closely as possible.
1 code implementation • 3 Jun 2020 • Hengrui Luo, Alice Patania, Jisu Kim, Mikael Vejdemo-Johansson
We provide simulation experiments and real data analysis to support our claim that circular coordinates with generalized penalty will detect the change in high-dimensional datasets under different sampling schemes while preserving the topological structures.
1 code implementation • 10 Oct 2019 • Hengrui Luo, Justin Strait
In the presence of images featuring objects with complex topological structures, such as objects with holes or multiple objects, the user must initialize separate curves for each boundary of interest.
1 code implementation • 23 Aug 2019 • Hengrui Luo, Giovanni Nattino, Matthew T. Pratola
In this paper we introduce a novel model for Gaussian process (GP) regression in the fully Bayesian setting.
Statistics Theory Statistics Theory