Search Results for author:
Found 5 papers, 2 papers with code
Augmenting Ground-Level PM2.5 Prediction via Kriging-Based Pseudo-Label Generation
We show that the proposed data augmentation strategy helps enhance the performance of the state-of-the-art convolutional neural network-random forest (CNN-RF) model by a reasonable amount, resulting in a noteworthy improvement in spatial correlation and a reduction in prediction error.
Causal Mediation Analysis with Multi-dimensional and Indirectly Observed Mediators
This is important in many scientific applications to identify the underlying mechanisms of a treatment effect.
Domain Adaptation via Rebalanced Sub-domain Alignment
To address this limitation, we propose a novel generalization bound that reweights source classification error by aligning source and target sub-domains.
Estimating Causal Effects using a Multi-task Deep Ensemble
A number of methods have been proposed for causal effect estimation, yet few have demonstrated efficacy in handling data with complex structures, such as images.
Incorporating Prior Knowledge into Neural Networks through an Implicit Composite Kernel
Many deep learning applications could be enhanced by modeling such known properties.
Ranked #1 on
Gaussian Processes
on UCI POWER
