Beyond Data Samples: Aligning Differential Networks Estimation with Scientific Knowledge
Learning the differential statistical dependency network between two contexts is essential for many real-life applications, mostly in the high dimensional low sample regime. In this paper, we propose a novel differential network estimator that allows integrating various sources of knowledge beyond data samples. The proposed estimator is scalable to a large number of variables and achieves a sharp asymptotic convergence rate. Empirical experiments on extensive simulated data and four real-world applications (one on neuroimaging and three from functional genomics) show that our approach achieves improved differential network estimation and provides better supports to downstream tasks like classification. Our results highlight significant benefits of integrating group, spatial and anatomic knowledge during differential genetic network identification and brain connectome change discovery.
Code (1)
Tasks
Network IdentificationStructured PredictionSimilar Papers 제목 키워드 기반
On the Estimation of Information Measures of Continuous Distributions
The estimation of information measures of continuous distributions based on samples is a fundamental problem in statistics and machine learning. In this paper, we analyze estimates of differential entropy in $K$-dimensio…
MetricDepth: Enhancing Monocular Depth Estimation with Deep Metric Learning
Deep metric learning aims to learn features relying on the consistency or divergence of class labels. However, in monocular depth estimation, the absence of a natural definition of class poses challenges in the leveragin…
Depth EstimationMetric LearningMonocular Depth EstimationInvestigating the Adversarial Robustness of Density Estimation Using the Probability Flow ODE
Beyond their impressive sampling capabilities, score-based diffusion models offer a powerful analysis tool in the form of unbiased density estimation of a query sample under the training data distribution. In this work, …
Adversarial RobustnessDensity EstimationOptimal Variance and Covariance Estimation under Differential Privacy in the Add-Remove Model and Beyond
In this paper, we study the problem of estimating the variance and covariance of datasets under differential privacy in the add-remove model. While estimation in the swap model has been extensively studied in the literat…
Distribution-Aware Mean Estimation under User-level Local Differential Privacy
We consider the problem of mean estimation under user-level local differential privacy, where $n$ users are contributing through their local pool of data samples. Previous work assume that the number of data samples is t…