Dehua Cheng

Dehua Cheng

Ph.D. Candidate

Computer Science Department
Viterbi School of Engineering
University of Southern California

Office: PHE 316

My name is Dehua Cheng (程德华). I am a PhD candidate in the Computer Science Department, of University of Southern California. I work on Machine Learning under supervision of Prof. Yan Liu.

My primary research interest lies in large scale machine learning. More specifically, I am interested in developing linear or sublinear numerical routines for machine learning algorithms by exploiting randomization and the structures of both the machine learning model and data. I have also worked on parallel inference for probabilistic graphical model, including topic models, Bayesian nonparametrics, etc. And in general, I am interested on (1) designing computational efficient models, and (2) improving existing machine learning algorithms from the computational efficiency aspect. My CV can be found here.

Education and Working Experience

Aug. 2012 - present

Ph.D. Candidate
Computer Science Department
University of Southern California
Advisor: Yan Liu

May 2017 - Aug. 2017

Software Engineer Intern
Feed Machine Learning @ Facebook
Advisor: Qichao Que

May 2016 - Aug. 2016

Research Intern
Thomas J Watson Research Center
IBM Research
Advisor: Jie Chen

Aug. 2008 - Jul. 2012

Mathematics and Physics
Tsinghua University



Michael Tsang, Dehua Cheng, and Yan Liu, Detecting Statistical Interactions from Neural Network Weights, arXiv:1705.04977.

Dehua Cheng*, Natali Ruchansky*, and Yan Liu (*Equal Contributions), Matrix Completion with Graphs: Identifying Completable Submatrices via Edge Connectivity.

Jie Chen, Dehua Cheng, Yan Liu, On Bochner's and Polya's Characterizations of Positive-Definite Kernels and the Respective Random Feature Maps, Submitted to JMLR. arXiv:1610.08861

Dehua Cheng, Yu Cheng, Yan Liu, Richard Peng, and Shang-Hua Teng, Spectral Sparsification of Random-Walk Matrix Polynomials, arXiv:1502.03496.

Dehua Cheng, Yu Cheng, Yan Liu, Richard Peng, and Shang-Hua Teng, Scalable Parallel Factorizations of SDD Matrices and Efficient Sampling for Gaussian Graphical Models, arXiv:1410.5392.

Refereed Publications

Dehua Cheng, Richard Peng, Ioakeim Perros, and Yan Liu, SPALS: Fast Alternating Least Squares via Implicit Leverage Scores Sampling, NIPS 2016. [PDF] [Code]

Dehua Cheng, Yu Cheng, Yan Liu, Richard Peng, and Shang-Hua Teng, Efficient Sampling for Gaussian Graphical Models via Spectral Sparsification, COLT 2015. [PDF].

Qi Yu, Dehua Cheng, and Yan Liu, Accelerated Online Low Rank Tensor Learning for Multivariate Spatiotemporal Streams, ICML 2015. [PDF]

Dehua Cheng*, Xinran He*, and Yan Liu (*Equal Contributions), Model Selection for Topic Models via Spectral Decomposition, AISTATS 2015. [PDF]

Dehua Cheng, Mohammad Taha Bahadori, and Yan Liu, FBLG: A Simple and Effective Approach for Temporal Dependence Discovery from Time Series Data, KDD 2014. [PDF]

Dehua Cheng, and Yan Liu, Parallel Gibbs Sampling for Hierarchical Dirichlet Processes via Gamma Processes Equivalence, KDD 2014. [PDF]


Workshop organizer, MiLeTs @ KDD ’16

Student Volunteer, ICML ’15

Student Volunteer, KDD ’14