Zobrazeno 1 - 10
of 57
pro vyhledávání: '"Chen, Elynn"'
Tensor classification has become increasingly crucial in statistics and machine learning, with applications spanning neuroimaging, computer vision, and recommendation systems. However, the high dimensionality of tensors presents significant challenge
Externí odkaz:
http://arxiv.org/abs/2409.14397
Breast cancer patients may experience relapse or death after surgery during the follow-up period, leading to dependent censoring of relapse. This phenomenon, known as semi-competing risk, imposes challenges in analyzing treatment effects on breast ca
Externí odkaz:
http://arxiv.org/abs/2407.01770
Information integration plays a pivotal role in biomedical studies by facilitating the combination and analysis of independent datasets from multiple studies, thereby uncovering valuable insights that might otherwise remain obscured due to the limite
Externí odkaz:
http://arxiv.org/abs/2407.00561
We introduce \underline{F}actor-\underline{A}ugmented \underline{Ma}trix \underline{R}egression (FAMAR) to address the growing applications of matrix-variate data and their associated challenges, particularly with high-dimensionality and covariate co
Externí odkaz:
http://arxiv.org/abs/2405.17744
As tensors become widespread in modern data analysis, Tucker low-rank Principal Component Analysis (PCA) has become essential for dimensionality reduction and structural discovery in tensor datasets. Motivated by the common scenario where large-scale
Externí odkaz:
http://arxiv.org/abs/2405.11681
In the evolving landscape of digital commerce, adaptive dynamic pricing strategies are essential for gaining a competitive edge. This paper introduces novel {\em doubly nonparametric random utility models} that eschew traditional parametric assumptio
Externí odkaz:
http://arxiv.org/abs/2405.06866
Matrix-variate data of high dimensions are frequently observed in finance and economics, spanning extended time periods, such as the long-term data on international trade flows among numerous countries. To address potential structural shifts and expl
Externí odkaz:
http://arxiv.org/abs/2404.01546
In data-driven decision-making in marketing, healthcare, and education, it is desirable to utilize a large amount of data from existing ventures to navigate high-dimensional feature spaces and address data scarcity in new ventures. We explore knowled
Externí odkaz:
http://arxiv.org/abs/2404.15209
Graph classification is an important learning task for graph-structured data. Graph neural networks (GNNs) have recently gained growing attention in graph learning and have shown significant improvements in many important graph problems. Despite thei
Externí odkaz:
http://arxiv.org/abs/2401.12007
Trading through decentralized exchanges (DEXs) has become crucial in today's blockchain ecosystem, enabling users to swap tokens efficiently and automatically. However, the capacity of miners to strategically order transactions has led to exploitativ
Externí odkaz:
http://arxiv.org/abs/2309.12640