17 citations · 74 across the 39 of their papers we have counts for
6 papers · 1 filter
GGMPs: Generalized Gaussian Mixture Processes
Vardaan Tekriwal, Mark D. Risser, Hengrui Luo +1
Conditional density estimation is complicated by multimodality, heteroscedasticity, and strong non-Gaussianity. Gaussian processes (GPs) provide a principled nonparametric framewor…
gp2Scale: A Class of Compactly Supported Non-Stationary Kernels and Distributed Computing for Exact Gaussian Processes on 10 Million Data Points
Marcus M. Noack, Mark D. Risser, Hengrui Luo +2
Despite a large corpus of recent work on scaling up Gaussian processes, a stubborn trade-off between computational speed, prediction and uncertainty quantification accuracy, and cu…
Efficient Decision Trees for Tensor Regressions
Hengrui Luo, Akira Horiguchi, Li Ma
We proposed the tensor-input tree (TT) method for scalar-on-tensor and tensor-on-tensor regression problems. We first address scalar-on-tensor problem by proposing scalar-output re…
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…
Hybrid Parameter Search and Dynamic Model Selection for Mixed-Variable Bayesian Optimization
Hengrui Luo, Younghyun Cho, James W. Demmel +2
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 qualita…
Non-smooth Bayesian Optimization in Tuning Problems
Hengrui Luo, James W. Demmel, Younghyun Cho +2
Building surrogate models is one common approach when we attempt to learn unknown black-box functions. Bayesian optimization provides a framework which allows us to build surrogate…