论文检索

高维多输出回归的联合稀疏迁移学习AI 解读

Multitask linear models can improve estimation and prediction by exploiting structure shared across responses, bridging taskwise fitting and complete pooling. In many applications, however, the objective is estimation in a data-limited target domain, while data-rich but heterogeneous source domains are available. Borrowing from these sources can improve efficiency but introduce bias. We develop a joint-sparse transfer-learning framework for high-dimensional multi-output…

SMART：面向多任务学习的谱迁移方法AI 解读

Multi-task learning is effective for related applications, but its performance can deteriorate when the target sample size is small. Transfer learning can borrow strength from related studies; yet, many existing methods rely on restrictive bounded-difference assumptions between the source and target models. We propose SMART, a spectral transfer method for multi-task linear regression that instead assumes spectral similarity: the target left and right singular subspaces lie…

长程依赖时间序列协方差矩阵与精度矩阵的同时推断AI 解读

For time series with long-range temporal dependence, inference for covariance and precision matrices is non-trivial. We propose a Berry-Esseen type Gaussian approximation result that gives a finite-sample bound for the Kolmogorov distance between the infinity norms of the estimation error of sample covariance matrix and the corresponding Gaussian approximation. The method utilizes martingale and m-dependent approximation and relies on constructing triadic blocks. We also…

因果排序的置信集合AI 解读

Causal discovery procedures aim to deduce causal relationships among variables in a multivariate dataset. While various methods have been proposed for estimating a single causal model or a single equivalence class of models, less attention has been given to quantifying uncertainty in causal discovery in terms of confidence statements. A primary challenge in causal discovery of directed acyclic graphs is determining a causal ordering among the variables, and our work offers a…