Research Interest

My research interest is in Machine Learning, especially in sparsity models, high-dimensional statistics, and learning theory.

Publications

Journal and Conference

• An Improved Analysis of Alternating Minimization for Structured Multi-Response Regression

Sheng Chen and Arindam Banerjee

Advances in Neural Information Processing Systems (NIPS), 2018

• Stable Gradient Descent

Yingxue Zhou, Sheng Chen, and Arindam Banerjee

Conference on Uncertainty in Artificial Intelligence (UAI), 2018 [pdf]

• Sparse Linear Isotonic Models

Sheng Chen and Arindam Banerjee

International Conference on Artificial Intelligence and Statistics (AISTATS), 2018 (plenary oral) [pdf]

• Alternating Estimation for Structured High-Dimensional Multi-Response Models

Sheng Chen and Arindam Banerjee

Advances in Neural Information Processing Systems (NIPS), 2017 [pdf]

• Robust Structured Estimation with Single-Index Models

Sheng Chen and Arindam Banerjee

International Conference on Machine Learning (ICML), 2017 [pdf]

• Structured Matrix Recovery via the Generalized Dantzig Selector

Sheng Chen and Arindam Banerjee

Advances in Neural Information Processing Systems (NIPS), 2016 [pdf]

• Structured Estimation with Atomic Norms: General Bounds and Applications

Sheng Chen and Arindam Banerjee

Advances in Neural Information Processing Systems (NIPS), 2015 [pdf]

• One-bit Compressed Sensing with the k-support Norm

Sheng Chen and Arindam Banerjee

International Conference on Artificial Intelligence and Statistics (AISTATS), 2015 [pdf]

• Generalized Dantzig Selector: Application to the k-support norm

Soumyadeep Chatterjee*, Sheng Chen*, and Arindam Banerjee (*equal contribution)

Advances in Neural Information Processing Systems (NIPS), 2014 [pdf]

• Estimation with Norm Regularization

Arindam Banerjee, Sheng Chen, Farideh Fazayeli, and Vidyashankar Sivakumar

Advances in Neural Information Processing Systems (NIPS), 2014 [pdf]

• A novel subnetwork alignment approach predicts new components of the cell cycle regulatory apparatus in Plasmodium falciparum

Hong Cai, Changjin Hong, Timothy G Lilburn, Armando L Rodriguez, Sheng Chen, Jianying Gu, Rui Kuang and Yufeng Wang

BMC Bioinformatics 2013, 14(Suppl 12):S2 [pdf]

Workshop and Others

• DocTag2Vec: An Embedding Based Multi-label Learning Approach for Document Tagging

Sheng Chen, Akshay Soni, Aasish Pappu, and Yashar Mehdad

ACL Workshop on Representation Learning for NLP (RepL4NLP), 2017 [pdf] [Yahoo! Research news]

• Open Problem: Restricted Eigenvalue Condition for Heavy Tailed Designs

Arindam Banerjee, Sheng Chen, and Vidyashankar Sivakumar

Conference on Learning Theory (COLT) Open Problem, 2015 [pdf]

Awards

• Student Travel Award, NIPS (2017)

• Student Travel Award, ICML (2017)

• Student Travel Award, NIPS (2015)

• Student Travel Award, NIPS (2014)

• College of Science & Engineering Graduate Fellowship, University of Minnesota (2012-2013)

Teaching

• • Spring 2014 Teaching assistant of CSCI 2011 Discrete Structures of Computer Science, University of Minnesota