no code implementations • NeurIPS 2018 • Rachel Cummings, Sara Krehbiel, Yajun Mei, Rui Tuo, Wanrong Zhang
The change-point detection problem seeks to identify distributional changes at an unknown change-point k* in a stream of data.
no code implementations • 24 Sep 2020 • Wanrong Zhang, Yajun Mei
In many real-world problems of real-time monitoring high-dimensional streaming data, one wants to detect an undesired event or change quickly once it occurs, but under the sampling control constraint in the sense that one might be able to only observe or use selected components data for decision-making per time step in the resource-constrained environments.
no code implementations • 12 Mar 2021 • Yuchen He, Namjoon Suh, Xiaoming Huo, Sungha Kang, Yajun Mei
We provide a set of sufficient conditions which guarantee that, from a single trajectory data denoised by a Local-Polynomial filter, the support of $\mathbf{c}(\lambda)$ asymptotically converges to the true signed-support associated with the underlying PDE for sufficiently many data and a certain range of $\lambda$.
no code implementations • 29 Sep 2021 • Yiling Luo, Xiaoming Huo, Yajun Mei
This paper studies the Stochastic Gradient Descent (SGD) algorithm in kernel regression.
no code implementations • 13 Jan 2022 • Hongzhen Tian, Reuven Zev Cohen, Chuck Zhang, Yajun Mei
For both simulation and testing cohorts, the proposed method is more effective, stable, interpretable, and computationally efficient on parameter estimation.
no code implementations • 10 Apr 2022 • Wanrong Zhang, Yajun Mei, Rachel Cummings
We also empirically validate our theoretical results on several synthetic databases, showing that our algorithms also perform well in practice.
no code implementations • 29 Apr 2022 • Yiling Luo, Xiaoming Huo, Yajun Mei
In addition, the Gradient Descent (GD) with a moderate or small step-size converges along the direction that corresponds to the smallest eigenvalue.
no code implementations • 29 Apr 2022 • Yiling Luo, Xiaoming Huo, Yajun Mei
On the other hand, algorithms such as gradient descent and stochastic gradient descent have the implicit regularization property that leads to better performance in terms of the generalization errors.
no code implementations • 9 Aug 2022 • Jiuyun Hu, Yajun Mei, Sarah Holte, Hao Yan
In this paper, we present an efficient statistical method (denoted as "Adaptive Resources Allocation CUSUM") to robustly and efficiently detect the hotspot with limited sampling resources.
no code implementations • 9 Aug 2022 • Xinyu Zhao, Jiuyun Hu, Yajun Mei, Hao Yan
High-dimensional data has become popular due to the easy accessibility of sensors in modern industrial applications.
no code implementations • 2 Dec 2022 • Yiling Luo, Xiaoming Huo, Yajun Mei
Our second estimator is a Hessian-free estimator that overcomes the aforementioned limitation.
no code implementations • 18 Sep 2023 • Ethan X. Fang, Yajun Mei, Yuyang Shi, Qunzhi Xu, Tuo Zhao
We consider the linear discriminant analysis problem in the high-dimensional settings.
no code implementations • 25 Oct 2023 • Zichong Li, Qunzhi Xu, Zhenghao Xu, Yajun Mei, Tuo Zhao, Hongyuan Zha
Specifically, our framework adopts a normalization-free objective by estimating the pseudolikelihood of marked STPPs through score-matching and offers uncertainty quantification for the predicted event time, location and mark by computing confidence regions over the generated samples.