“Discovering Latent Factors in High-dimensional Data via Spectral Methods”

Thu Nov 16, 2017 2:30 PM

Location: LTS Auditorium, 8080 Greenmead Drive

Furong Huang
Assistant professor, Department of Computer Science and UMIACS

Latent variable models have a broad set of applications in domains such as social networks, natural language processing, computer vision and computational biology. Training them on a large scale is challenging due to non-convexity of the objective function.

We propose a unified framework that exploits tensor algebraic constraints of the (low order) moments of the models. This versatile framework is guaranteed to estimate the correct model consistently and the spectral decomposition (matrix/tensor decomposition) proposed are embarrassingly parallel and has global convergence guarantees using SGD despite the non-convexity of the objective function.

Topic modeling will be discussed extensively as well as user commonality inference in the large-scale social network using Mixed Membership Stochastic Blockmodel and convolutional dictionary learning for text paraphrase embedding learning.

Speaker Bio:
Furong Huang is an assistant professor in the Department of Computer Science at the University of Maryland.
Her research interests lie in developing scalable and parallel algorithms for large-scale data using statistical models.

Huang has worked on non-convex function optimization such as finding tensor decomposition with global convergence guarantee using stochastic gradient descent, developing fast detection algorithms to discover hidden and overlapping user communities in social networks, designing a parallel spectral tensor decomposition algorithm for detecting hidden topics in articles on Map-Reduce frameworks, and learning convolutional sparse coding models using tensor methods for extracting text sequence embeddings and image filter-bank.

Besides pure statistical computation, Huang has applied her machine learning techniques to biology.

She received her doctorate in electrical and computer engineering from the University of California, Irvine in 2016.