4 citations · 8 across the 10 of their papers we have counts for
10 papers
Effect-Wise Inference for Smoothing Spline ANOVA on Tensor-Product Sobolev Space
Youngjin Cho, Meimei Liu
Functional ANOVA provides a nonparametric modeling framework for multivariate covariates, enabling flexible estimation and interpretation of effect functions such as main effects a…
Scalable Statistical Inference in Non-parametric Least Squares
Meimei Liu, Zuofeng Shang, Yun Yang
Stochastic approximation (SA) is a powerful and scalable computational method for iteratively estimating the solution of optimization problems in the presence of randomness, partic…
Motion-Invariant Variational Auto-Encoding of Brain Structural Connectomes
Yizi Zhang, Meimei Liu, Zhengwu Zhang +1
Mapping of human brain structural connectomes via diffusion MRI offers a unique opportunity to understand brain structural connectivity and relate it to various human traits, such…
Robust Flow-based Conformal Inference (FCI) with Statistical Guarantee
Youhui Ye, Meimei Liu, Xin Xing
Conformal prediction aims to determine precise levels of confidence in predictions for new objects using past experience. However, the commonly used exchangeable assumptions betwee…
DVE: Dynamic Variational Embeddings with Applications in Recommender Systems
Meimei Liu, Hongxia Yang
Embedding is a useful technique to project a high-dimensional feature into a low-dimensional space, and it has many successful applications including link prediction, node classifi…
Exploration-Exploitation Motivated Variational Auto-Encoder for Recommender Systems
Yizi Zhang, Meimei Liu
Recent years have witnessed rapid developments on collaborative filtering techniques for improving the performance of recommender systems due to the growing need of companies to he…
Domain Adaptive Bootstrap Aggregating
Meimei Liu, David B. Dunson
When there is a distributional shift between data used to train a predictive algorithm and current data, performance can suffer. This is known as the domain adaptation problem. Boo…
Auto-encoding brain networks with applications to analyzing large-scale brain imaging datasets
Meimei Liu, Zhengwu Zhang, David B. Dunson
There has been huge interest in studying human brain connectomes inferred from different imaging modalities and exploring their relationship with human traits, such as cognition. B…
Early Stopping for Nonparametric Testing
Meimei Liu, Guang Cheng
Early stopping of iterative algorithms is an algorithmic regularization method to avoid over-fitting in estimation and classification. In this paper, we show that early stopping ca…
How Many Machines Can We Use in Parallel Computing for Kernel Ridge Regression?
Meimei Liu, Zuofeng Shang, Guang Cheng
This paper aims to solve a basic problem in distributed statistical inference: how many machines can we use in parallel computing? In kernel ridge regression, we address this quest…