Yuchen Zhang



I am a research scientist at Semantic Machines. I work on machine learning and natural language processing, with the goal of building the next-generation dialogue systems.

Previously, I was a post-doc researcher at Stanford University, hosted by Percy Liang and Moses Charikar. I got Ph.D. in computer science from UC Berkeley, where I was very fortunate to be advised by Michael Jordan and Martin Wainwright. Before that, I got a Master in statistics from Berkeley and a Bachelor in computer science from Tsinghua University. I did several internships at Microsoft Research, Google and Baidu, and collaborated with Lin Xiao, Weizhu Chen and Vanja Josifovski.

Email: zhangyuc (at) gmail (dot) com    [Google Scholar]
 
 
Manuscripts
 
Splash: User-friendly Programming Interface for Parallelizing Stochastic Algorithms [arXiv] [project website] [GitHub]
Y. Zhang and MI. Jordan
 
Optimality Guarantees for Distributed Statistical Estimation [arXiv]
J. Duchi, MI. Jordan, M. Wainwright, Y. Zhang
 
 
Journal Publications
 
Stochastic Primal-Dual Coordinate Method for Regularized Empirical Risk Minimization [arXiv]
Y. Zhang, L. Xiao
Journal of Machine Learning Research
 
A Note on the Approximate Admissibility of Regularized Estimators in the Gaussian Sequence Model [arXiv]
X. Chen, A. Guntuboyina, Y. Zhang
Electronic Journal of Statistics
 
Optimal Prediction for Sparse Linear Models? Lower Bounds for Coordinate-separable M-estimator [pdf]
Y. Zhang, M. Wainwright, MI. Jordan
Electronic Journal of Statistics
 
On Bayes Risk Lower Bounds [pdf]
X. Chen, A. Guntuboyina, Y. Zhang
Journal of Machine Learning Research
 
Spectral Methods meet EM: A Provably Optimal Algorithm for Crowdsourcing [pdf] [GitHub]
Y. Zhang , X. Chen, D. Zhou, MI. Jordan
Journal of Machine Learning Research
 
Divide and Conquer Kernel Ridge Regression: A Distributed Algorithm with Minimax Optimal Rates [pdf]
Y. Zhang , J. Duchi, M. Wainwright
Journal of Machine Learning Research
 
Communication-Efficient Algorithms for Statistical Optimization [pdf]
Y. Zhang , J. Duchi, M. Wainwright
Journal of Machine Learning Research
 
The Antimagicness of the Cartesian Product of Graphs [pdf]
Y. Zhang and X. Sun
Theoretical Computer Science
 
 
Conference Proceedings
 
Macro Grammars and Holistic Triggering for Efficient Semantic Parsing [pdf] [CodaLab]
Y. Zhang, P. Pasupat, P. Liang
Empirical Methods on Natural Language Processing (EMNLP'17)
 
Convexified Convolutional Neural Networks [pdf] [arXiv] [GitHub] [CodaLab]
Y. Zhang, P. Liang, M. Wainwright
International Conference on Machine Learning (ICML'17)
 
A Hitting Time Analysis of Stochastic Gradient Langevin Dynamics [pdf] [arXiv]
Y. Zhang, P. Liang, M. Charikar
Conference on Learning Theory (COLT'17)    Best Paper Award
 
On the Learnability of Fully-connected Neural Networks [pdf] [arXiv]
Y. Zhang, JD. Lee, M. Wainwright, MI. Jordan
Artificial Intelligence and Statistics (AISTATS'17)
 
Local Maxima in the Likelihood of Gaussian Mixture Models: Structural Results and Algorithmic Consequences [pdf] [arXiv]
C. Jin, Y. Zhang, S. Balakrishnan, M. Wainwright, MI. Jordan
Neural Information Processing System (NIPS'16)
 
L1-regularized Neural Networks are Improperly Learnable in Polynomial Time [pdf] [arXiv]
Y. Zhang, JD. Lee, MI. Jordan
International Conference on Machine Learning (ICML'16)
 
Distributed Estimation of Generalized Matrix Rank: Efficient Algorithms and Lower Bounds [pdf] [arXiv]
Y. Zhang, M. Wainwright, MI. Jordan
International Conference on Machine Learning (ICML'15)
 
DiSCO: Communication-Efficient Distributed Optimization of Self-Concordant Loss [pdf] [arXiv]
Y. Zhang, L. Xiao
International Conference on Machine Learning (ICML'15)
 
Stochastic Primal-Dual Coordinate Method for Regularized Empirical Risk Minimization [pdf]
Y. Zhang, L. Xiao
International Conference on Machine Learning (ICML'15)
 
Spectral Methods meet EM: A Provably Optimal Algorithm for Crowdsourcing [pdf]
Y. Zhang, X. Chen, D. Zhou, MI. Jordan
Neural Information Processing System (NIPS'14)
 
Lower Bounds on the Performance of Polynomial-time Algorithms for Sparse Linear Regression [pdf] [arXiv]
Y. Zhang, M. Wainwright, MI. Jordan
Conference on Learning Theory (COLT'14)
 
Taxonomy Discovery for Personalized Recommendation [pdf]
Y. Zhang A. Ahmed, V. Josifovski, A. Smola
ACM International Conference on Web Search and Data Mining (WSDM'14)
 
Information-theoretic Lower Bounds for Distributed Statistical Estimation with Communication Constraints [pdf]
Y. Zhang, J. Duchi, MI. Jordan, M. Wainwright
Neural Information Processing System (NIPS'13)
 
Divide and Conquer Kernel Ridge Regression [pdf]
Y. Zhang , J. Duchi, M. Wainwright
Conference on Learning Theory (COLT'13)
 
Communication-Efficient Algorithms for Statistical Optimization [pdf]
Y. Zhang , J. Duchi, M. Wainwright
Neural Information Processing System (NIPS'12)
 
A Noise-aware Click Model for Web Search [pdf]
W. Chen, D. Wang, Y. Zhang, Q. Yang
ACM International Conference on Web Search and Data Mining (WSDM'12)
 
User-click Modeling for Understanding and Predicting Search-behavior [pdf]
Y. Zhang, W. Chen, D. Wang, Q. Yang
ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD'11)
 
Characterize Search Intent Diversity into Click Models [pdf]
B. Hu, Y. Zhang, W. Chen, G. Wang, Q. Yang
International World Wide Web Conference (WWW'11)
 
Learning Click Model via Probit Bayesian Inference [pdf]
Y. Zhang, D. Wang, G. Wang, W. Chen, Z. Zhang, B. Hu, L. Zhang
ACM Conference on Information and Knowledge Management (CIKM'10)
 
Explore click models for search ranking [pdf]
D. Wang, W. Chen, G. Wang, Y. Zhang, B. Hu
ACM Conference on Information and Knowledge Management (CIKM'10)
 
Incorporating Post-Click Behaviors Into a Click Model [pdf]
F. Zhong, D. Wang, G. Wang, W. Chen, Y. Zhang, Z. Chen, H. Wang
ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR'10)
 
Extracting Independent Rules: a New Perspective of Boosting [pdf]
Y. Zhang and L. Zhang
International Symposium on Artificial Intelligence and Mathematics (ISAIM'10)