17 citations · 24 across the 5 of their papers we have counts for
5 papers
Topological Learning for Motion Data via Mixed Coordinates
Hengrui Luo, Jisu Kim, Alice Patania +1
Topology can extract the structural information in a dataset efficiently. In this paper, we attempt to incorporate topological information into a multiple output Gaussian process m…
Sharded Bayesian Additive Regression Trees
Hengrui Luo, Matthew T. Pratola
In this paper we develop the randomized Sharded Bayesian Additive Regression Trees (SBT) model. We introduce a randomization auxiliary variable and a sharding tree to decide partit…
Efficient and Robust Bayesian Selection of Hyperparameters in Dimension Reduction for Visualization
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 perfo…
Contrastive inverse regression for dimension reduction
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 functiona…
Randomized Numerical Linear Algebra : A Perspective on the Field With an Eye to Software
Riley Murray, James Demmel, Michael W. Mahoney +10
Randomized numerical linear algebra - RandNLA, for short - concerns the use of randomization as a resource to develop improved algorithms for large-scale linear algebra computation…