7 citations · 10 across the 5 of their papers we have counts for
7 papers
Bayesian Discovery of Multiple Bayesian Networks via Transfer Learning
Diane Oyen, Terran Lane
Bayesian network structure learning algorithms with limited data are being used in domains such as systems biology and neuroscience to gain insight into the underlying processes th…
Smoothness and Structure Learning by Proxy
Benjamin Yackley, Terran Lane
As data sets grow in size, the ability of learning methods to find structure in them is increasingly hampered by the time needed to search the large spaces of possibilities and gen…
Active Learning for Node Classification in Assortative and Disassortative Networks
Cristopher Moore, Xiaoran Yan, Yaojia Zhu +2
In many real-world networks, nodes have class labels, attributes, or variables that affect the network's topology. If the topology of the network is known but the labels of the nod…
Directional Statistics on Permutations
Sergey M. Plis, Terran Lane, Vince D. Calhoun
Distributions over permutations arise in applications ranging from multi-object tracking to ranking of instances. The difficulty of dealing with these distributions is caused by th…
Multiplicative updates For Non-Negative Kernel SVM
Vamsi K. Potluru, Sergey M. Plis, Morten Morup +2
We present multiplicative updates for solving hard and soft margin support vector machines (SVM) with non-negative kernels. They follow as a natural extension of the updates for no…
Parameters Affecting the Resilience of Scale-Free Networks to Random Failures
Hamilton Link, Randall A. LaViolette, Jared Saia +1
It is commonly believed that scale-free networks are robust to massive numbers of random node deletions. For example, Cohen et al. study scale-free networks including some which ap…