Zobrazeno 1 - 10
of 285
pro vyhledávání: '"Allen, Genevera"'
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
Ensemble methods, particularly boosting, have established themselves as highly effective and widely embraced machine learning techniques for tabular data. In this paper, we aim to leverage the robust predictive power of traditional boosting methods w
Externí odkaz:
http://arxiv.org/abs/2404.01521
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
Across various sectors such as healthcare, criminal justice, national security, finance, and technology, large-scale machine learning (ML) and artificial intelligence (AI) systems are being deployed to make critical data-driven decisions. Many have a
Externí odkaz:
http://arxiv.org/abs/2310.04352
In this work, we propose data augmentation via pairwise mixup across subgroups to improve group fairness. Many real-world applications of machine learning systems exhibit biases across certain groups due to under-representation or training data that
Externí odkaz:
http://arxiv.org/abs/2309.07110
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, 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
As a tool for estimating networks in high dimensions, graphical models are commonly applied to calcium imaging data to estimate functional neuronal connectivity, i.e. relationships between the activities of neurons. However, in many calcium imaging d
Externí odkaz:
http://arxiv.org/abs/2209.08273
To promote new scientific discoveries from complex data sets, feature importance inference has been a long-standing statistical problem. Instead of testing for parameters that are only interpretable for specific models, there has been increasing inte
Externí odkaz:
http://arxiv.org/abs/2206.02088