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Found 13 papers, 0 papers with code
Score Matching-based Pseudolikelihood Estimation of Neural Marked Spatio-Temporal Point Process with Uncertainty Quantification
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.
Pivotal Estimation of Linear Discriminant Analysis in High Dimensions
We consider the linear discriminant analysis problem in the high-dimensional settings.
Covariance Estimators for the ROOT-SGD Algorithm in Online Learning
Our second estimator is a Hessian-free estimator that overcomes the aforementioned limitation.
Adaptive Resources Allocation CUSUM for Binomial Count Data Monitoring with Application to COVID-19 Hotspot Detection
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.
Adaptive Partially-Observed Sequential Change Detection and Isolation
High-dimensional data has become popular due to the easy accessibility of sensors in modern industrial applications.
The Directional Bias Helps Stochastic Gradient Descent to Generalize in Kernel Regression Models
In addition, the Gradient Descent (GD) with a moderate or small step-size converges along the direction that corresponds to the smallest eigenvalue.
Implicit Regularization Properties of Variance Reduced Stochastic Mirror Descent
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.
Private Sequential Hypothesis Testing for Statisticians: Privacy, Error Rates, and Sample Size
We also empirically validate our theoretical results on several synthetic databases, showing that our algorithms also perform well in practice.
Active Learning-Based Multistage Sequential Decision-Making Model with Application on Common Bile Duct Stone Evaluation
For both simulation and testing cohorts, the proposed method is more effective, stable, interpretable, and computationally efficient on parameter estimation.
Directional Bias Helps Stochastic Gradient Descent to Generalize in Nonparametric Model
This paper studies the Stochastic Gradient Descent (SGD) algorithm in kernel regression.
Asymptotic Theory of $\ell_1$-Regularized PDE Identification from a Single Noisy Trajectory
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 $\lambda$.
Bandit Change-Point Detection for Real-Time Monitoring High-Dimensional Data Under Sampling Control
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.
Differentially Private Change-Point Detection
The change-point detection problem seeks to identify distributional changes at an unknown change-point k* in a stream of data.
