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pro vyhledávání: '"Zheng, Lili"'
Patchwork learning arises as a new and challenging data collection paradigm where both samples and features are observed in fragmented subsets. Due to technological limits, measurement expense, or multimodal data integration, such patchwork data stru
Externí odkaz:
http://arxiv.org/abs/2406.13833
Multi-modal populations of networks arise in many scenarios including in large-scale multi-modal neuroimaging studies that capture both functional and structural neuroimaging data for thousands of subjects. A major research question in such studies i
Externí odkaz:
http://arxiv.org/abs/2312.14416
New technologies have led to vast troves of large and complex datasets across many scientific domains and industries. People routinely use machine learning techniques to not only process, visualize, and make predictions from this big data, but also t
Externí odkaz:
http://arxiv.org/abs/2308.01475
Probabilistic graphical models have become an important unsupervised learning tool for detecting network structures for a variety of problems, including the estimation of functional neuronal connectivity from two-photon calcium imaging data. However,
Externí odkaz:
http://arxiv.org/abs/2305.13491
Autor:
Zheng, Lili, Raskutti, Garvesh
Classification with positive and unlabeled (PU) data frequently arises in bioinformatics, clinical data, and ecological studies, where collecting negative samples can be prohibitively expensive. While prior works on PU data focus on binary classifica
Externí odkaz:
http://arxiv.org/abs/2304.09305
Autor:
Zheng, Lili
Publikováno v:
Information Technology & People, 2023, Vol. 37, Issue 3, pp. 1360-1378.
Externí odkaz:
http://www.emeraldinsight.com/doi/10.1108/ITP-06-2022-0465
Accurate indoor crowd counting (ICC) is a key enabler to many smart home/office applications. In this paper, we propose a Domain-Agnostic and Sample-Efficient wireless indoor crowd Counting (DASECount) framework that suffices to attain robust cross-d
Externí odkaz:
http://arxiv.org/abs/2211.10040
Autor:
Zheng, Lili, Allen, Genevera I.
In this paper, we investigate the Gaussian graphical model inference problem in a novel setting that we call erose measurements, referring to irregularly measured or observed data. For graphs, this results in different node pairs having vastly differ
Externí odkaz:
http://arxiv.org/abs/2210.11625