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Found 7 papers, 2 papers with code
ReTaSA: A Nonparametric Functional Estimation Approach for Addressing Continuous Target Shift
This work focuses on the target shift problem in a regression setting (Zhang et al., 2013; Nguyen et al., 2016).
Channel-Feedback-Free Transmission for Downlink FD-RAN: A Radio Map based Complex-valued Precoding Network Approach
As the demand for high-quality services proliferates, an innovative network architecture, the fully-decoupled RAN (FD-RAN), has emerged for more flexible spectrum resource utilization and lower network costs.
Assumption-lean and Data-adaptive Post-Prediction Inference
A primary challenge facing modern scientific research is the limited availability of gold-standard data which can be both costly and labor-intensive to obtain.
Sufficient Identification Conditions and Semiparametric Estimation under Missing Not at Random Mechanisms
This MNAR model corresponds to a so-called criss-cross structure considered in the literature on graphical models of missing data that prevents nonparametric identification of the entire missing data model.
ELSA: Efficient Label Shift Adaptation through the Lens of Semiparametric Models
We study the domain adaptation problem with label shift in this work.
Optimal and Safe Estimation for High-Dimensional Semi-Supervised Learning
Our goal is to investigate when and how the unlabeled data can be exploited to improve the estimation of the regression parameters of linear model in light of the fact that such linear models may be misspecified in data analysis.
Nonregular and Minimax Estimation of Individualized Thresholds in High Dimension with Binary Responses
Statistically, we show that the finite sample error bound for estimating $\theta$ in $\ell_2$ norm is $(s\log d/n)^{\beta/(2\beta+1)}$, where $d$ is the dimension of $\theta$, $s$ is the sparsity level, $n$ is the sample size and $\beta$ is the smoothness of the conditional density of $X$ given the response $Y$ and the covariates $Z$.
