Computer Science - Machine Learning

Model Selection for Topic Models via Spectral Decomposition
Topic models have achieved significant successes in analyzing large-scale text corpus. In practical applications, we are always …
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Spectral Sparsification of Random-Walk Matrix Polynomials
We consider a fundamental algorithmic question in spectral graph theory: Compute a spectral sparsifier of random-walk matrix-polynomial …
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Scalable Parallel Factorizations of SDD Matrices and Efficient Sampling for Gaussian Graphical Models
Motivated by a sampling problem basic to computational statistical inference, we develop a nearly optimal algorithm for a fundamental …
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Transfer Topic Modeling with Ease and Scalability
The increasing volume of short texts generated on social media sites, such as Twitter or Facebook, creates a great demand for effective …
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Sparse-GEV: Sparse Latent Space Model for Multivariate Extreme Value Time Serie Modeling
In many applications of time series models, such as climate analysis and social media analysis, we are often interested in extreme …
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