3 papers
stat.ML2024
Causal inference through multi-stage learning and doubly robust deep neural networks
Yuqian Zhang, Jelena Bradic
Deep neural networks (DNNs) have demonstrated remarkable empirical performance in large-scale supervised learning problems, particularly in scenarios where both the sample size …
stat.ML2024
Adaptive Split Balancing for Optimal Random Forest
Yuqian Zhang, Weijie Ji, Jelena Bradic
In this paper, we propose a new random forest algorithm that constructs the trees using a novel adaptive split-balancing method. Rather than relying on the widely-used random featu…
stat.ME2023
Semi-supervised linear regression: enhancing efficiency and robustness in high dimensions
Kai Chen, Yuqian Zhang
In semi-supervised learning, the prevailing understanding suggests that observing additional unlabeled samples improves estimation accuracy for linear parameters only in the case o…