Zobrazeno 1 - 10
of 91
pro vyhledávání: '"Han, Qiyang"'
Autor:
Han, Qiyang
General first order methods (GFOMs), including various gradient descent and AMP algorithms, constitute a broad class of iterative algorithms in modern statistical learning problems. Some GFOMs also serve as constructive proof devices, iteratively cha
Externí odkaz:
http://arxiv.org/abs/2406.19061
Approximate message passing (AMP) has emerged both as a popular class of iterative algorithms and as a powerful analytic tool in a wide range of statistical estimation problems and statistical physics models. A well established line of AMP theory pro
Externí odkaz:
http://arxiv.org/abs/2312.05911
Autor:
Han, Qiyang, Xu, Xiaocong
The Ridgeless minimum $\ell_2$-norm interpolator in overparametrized linear regression has attracted considerable attention in recent years. While it seems to defy the conventional wisdom that overfitting leads to poor prediction, recent research rev
Externí odkaz:
http://arxiv.org/abs/2307.02044
Autor:
Han, Qiyang, Ren, Huachen
Understanding the stochastic behavior of random projections of geometric sets constitutes a fundamental problem in high dimension probability that finds wide applications in diverse fields. This paper provides a kinematic description for the behavior
Externí odkaz:
http://arxiv.org/abs/2212.05545
Autor:
Han, Qiyang
Let $Z_1,\ldots,Z_n$ be i.i.d. isotropic random vectors in $\mathbb{R}^p$, and $T \subset \mathbb{R}^p$ be a compact set. A classical line of empirical process theory characterizes the size of the suprema of the quadratic process \begin{align*} \sup_
Externí odkaz:
http://arxiv.org/abs/2207.13594
Autor:
Han, Qiyang, Shen, Yandi
The Convex Gaussian Min-Max Theorem (CGMT) has emerged as a prominent theoretical tool for analyzing the precise stochastic behavior of various statistical estimators in the so-called high dimensional proportional regime, where the sample size and th
Externí odkaz:
http://arxiv.org/abs/2206.07936
Autor:
Han, Qiyang
In the standard Gaussian linear measurement model $Y=X\mu_0+\xi \in \mathbb{R}^m$ with a fixed noise level $\sigma>0$, we consider the problem of estimating the unknown signal $\mu_0$ under a convex constraint $\mu_0 \in K$, where $K$ is a closed con
Externí odkaz:
http://arxiv.org/abs/2201.08435
We consider the nonparametric estimation of an S-shaped regression function. The least squares estimator provides a very natural, tuning-free approach, but results in a non-convex optimisation problem, since the inflection point is unknown. We show t
Externí odkaz:
http://arxiv.org/abs/2107.07257
Autor:
Han, Qiyang, Shen, Yandi
Distance covariance is a popular dependence measure for two random vectors $X$ and $Y$ of possibly different dimensions and types. Recent years have witnessed concentrated efforts in the literature to understand the distributional properties of the s
Externí odkaz:
http://arxiv.org/abs/2106.07725
Autor:
Han, Qiyang
The theory for multiplier empirical processes has been one of the central topics in the development of the classical theory of empirical processes, due to its wide applicability to various statistical problems. In this paper, we develop theory and to
Externí odkaz:
http://arxiv.org/abs/2102.05764