Multiple graph regularized protein domain ranking

Regularized Autoregressive Multiple Frequency Estimation

The paper addresses a problem of tracking multiple number of frequencies using Regularized Autoregressive (RAR) approximation. The RAR procedure allows to decrease approximation bias, comparing to other AR-based frequency detection methods, while still providing competitive variance of sample estimates. We show that the RAR estimates of multiple periodicities are consistent in probabilit...

Large Scale Co-Regularized Ranking

As unlabeled data is usually easy to collect, semisupervised learning algorithms that can be trained on large amounts of unlabeled and labeled data are becoming increasingly popular for ranking and preference learning problems [6, 23, 8, 21]. However, the computational complexity of the vast majority of these (pairwise) ranking and preference learning methods is super-linear, as optimizing an o...

Adversarially Regularized Graph Autoencoder

Graph embedding is an e‚ective method to represent graph data in a low dimensional space for graph analytics. Most existing embedding algorithms typically focus on preserving the topological structure or minimizing the reconstruction errors of graph data, but they have mostly ignored the data distribution of the latent codes from the graphs, which o‰en results in inferior embedding in real-worl...

Transductive Ranking via Pairwise Regularized Least-Squares

Regularized Least-Squares for Parse Ranking

We present an adaptation of the Regularized Least-Squares algorithm for the rank learning problem and an application of the method to reranking of the parses produced by the Link Grammar (LG) dependency parser. We study the use of several grammatically motivated features extracted from parses and evaluate the ranker with individual features and the combination of all features on a set of biomed...