Search Results for author:
Found 7 papers, 5 papers with code
Covariance regression with random forests
Capturing the conditional covariances or correlations among the elements of a multivariate response vector based on covariates is important to various fields including neuroscience, epidemiology and biomedicine.
Bayesian Complementary Kernelized Learning for Multidimensional Spatiotemporal Data
Probabilistic modeling of multidimensional spatiotemporal data is critical to many real-world applications.
Spatial Aggregation and Temporal Convolution Networks for Real-time Kriging
Specifically, we propose a novel spatial aggregation network (SAN) inspired by Principal Neighborhood Aggregation, which uses multiple aggregation functions to help one node gather diverse information from its neighbors.
Scalable Spatiotemporally Varying Coefficient Modelling with Bayesian Kernelized Tensor Regression
To address this challenge, we summarize the spatiotemporally varying coefficients using a third-order tensor structure and propose to reformulate the spatiotemporally varying coefficient model as a special low-rank tensor regression problem.
RFpredInterval: An R Package for Prediction Intervals with Random Forests and Boosted Forests
The set of methods implemented in the package includes a new method to build prediction intervals with boosted forests (PIBF) and 15 method variations to produce prediction intervals with random forests, as proposed by Roy and Larocque (2020).
Conditional canonical correlation estimation based on covariates with random forests
We propose a new method called Random Forest with Canonical Correlation Analysis (RFCCA) to estimate the conditional canonical correlations between two sets of variables given subject-related covariates.
Inductive Graph Neural Networks for Spatiotemporal Kriging
Time series forecasting and spatiotemporal kriging are the two most important tasks in spatiotemporal data analysis.
