3 citations · 7 across the 7 of their papers we have counts for
7 papers
Locally Adaptive and Differentiable Regression
Mingxuan Han, Varun Shankar, Jeff M Phillips +1
Over-parameterized models like deep nets and random forests have become very popular in machine learning. However, the natural goals of continuity and differentiability, common in…
The ART of Transfer Learning: An Adaptive and Robust Pipeline
Boxiang Wang, Yunan Wu, Chenglong Ye
Transfer learning is an essential tool for improving the performance of primary tasks by leveraging information from auxiliary data resources. In this work, we propose Adaptive Rob…
High-dimensional Variable Screening via Conditional Martingale Difference Divergence
Lei Fang, Qingcong Yuan, Xiangrong Yin +1
Variable screening has been a useful research area that deals with ultrahigh-dimensional data. When there exist both marginally and jointly dependent predictors to the response, ex…
Meta Clustering for Collaborative Learning
Chenglong Ye, Reza Ghanadan, Jie Ding
In collaborative learning, learners coordinate to enhance each of their learning performances. From the perspective of any learner, a critical challenge is to filter out unqualifie…
Combining Predictions of Auto Insurance Claims
Chenglong Ye, Lin Zhang, Mingxuan Han +3
This paper aims to better predict highly skewed auto insurance claims by combining candidate predictions. We analyze a version of the Kangaroo Auto Insurance company data and study…
High-dimensional Adaptive Minimax Sparse Estimation with Interactions
Chenglong Ye, Yuhong Yang
High-dimensional linear regression with interaction effects is broadly applied in research fields such as bioinformatics and social science. In this paper, we first investigate the…
Sparsity Oriented Importance Learning for High-dimensional Linear Regression
Chenglong Ye, Yi Yang, Yuhong Yang
With now well-recognized non-negligible model selection uncertainty, data analysts should no longer be satisfied with the output of a single final model from a model selection proc…