Yaoliang Yu  —  Publications

2017  — 

2014  —  2016

  • V. Ganapathiraman, X. Zhang, Y. Yu, and J. Wen, Convex Two-Layer Modeling with Latent Structure. NIPS 2016. [pdf] [bibtex]

  • P. Xie, J. Kim, Y. Zhou, Q. Ho, A. Kumar, Y. Yu, and E. Xing, Lighter-Communication Distributed Machine Learning via Sufficient Factor Broadcasting. UAI 2016. [pdf] [bibtex]

  • K. Kandasamy and Y. Yu, Additive Approximations in High Dimensional Nonparametric Regression via the SALSA. ICML 2016. [pdf] [bibtex]

  • X. Chang, Y. Yu, Y. Yang, and E. Xing, They Are Not Equally Reliable: Semantic Event Search using Differentiated Concept Classifiers. CVPR 2016. [pdf] [bibtex]

  • M. Law, Y. Yu, M. Cord and E. Xing, Closed-Form Training of Mahalanobis Metric for Supervised Clustering. CVPR 2016. [pdf] [bibtex]

  • H. Cheng, Y. Yu, X. Zhang, E. Xing, and D. Schuurmans, Scalable and Sound Low-Rank Tensor Learning. AISTATS 2016. [pdf] [bibtex] [code]

  • E. Xing, Q. Ho, W. Dai, J. Kim, J. Wei, S. Lee, X. Zheng, P. Xie, A. Kumar, and Y. Yu, Petuum: A New Platform for Distributed Machine Learning on Big Data. IEEE Transactions on Big Data, vol. 1, no. 2, pp. 49-67, 2015. [pdf] [bibtex] [code]

  • X. Chang, Y. Yu, Y. Yang, and A. Hauptmann, Searching Persuasively: Joint Event Detection and Evidence Justification with Limited Supervision. ACM MM, 2015. [pdf] [bibtex]

  • X. Zheng, Y. Yu, and E. Xing, Linear Time Samplers for Supervised Topic Models using Compositional Proposals. ACM KDD 2015. [pdf] [bibtex] [code]

  • X. Chang, Y. Yang, A. Hauptmann, E. Xing, and Y.Yu, Semantic Concept Discovery for Large-Scale Zero-Shot Event Detection. IJCAI 2015. [pdf] [bibtex]

  • Y. Yu, X. Zheng, M. Marchetti-Bowick and E. Xing, Minimizing Nonconvex Non-Separable Functions. AISTATS 2015. [pdf] [appendix] [bibtex]

  • A. Yu, W. Ma, Y. Yu, J. Carbonell and S. Sra, Efficient Structured Matrix Rank Minimization. NIPS 2014. [pdf] [bibtex]

2008  —  2013

  • Y. Yu, Better Approximation and Faster Algorithm Using the Proximal Average. NIPS 2013. [pdf] [bibtex]

  • Y. Yu, On Decomposing the Proximal Map. NIPS 2013. [pdf] [bibtex]

  • X. Zhang, Y. Yu and D. Schuurmans, Polar Operators for Structured Sparse Estimation. NIPS 2013. [pdf] [bibtex]

  • Y. Yu, H. Cheng, D. Schuurmans and C. Szepesvári, Characterizing the Representer Theorem. ICML 2013. [pdf] [bibtex]

  • X. Zhang, Y. Yu and D. Schuurmans, Accelerated Training for Matrix-norm Regularization: A Boosting Approach. NIPS 2012. [pdf] [bibtex] [code]

  • M. White, Y. Yu, X. Zhang and D. Schuurmans, Convex Multi-view Subspace Learning. NIPS 2012. [pdf] [bibtex]

  • Y. Yu, O. Aslan and D. Schuurmans, A Polynomial-time Form of Robust Regression. NIPS 2012. [pdf] [bibtex]

  • Y. Yu and C. Szepesvári, Analysis of Kernel Mean Matching under Covariate Shift. ICML 2012. [pdf] [bibtex]

  • Y. Yu, J. Neufeld, R. Kyros, X. Zhang and D. Schuurmans, Regularizers versus Losses for Nonlinear Dimensionality Reduction. ICML 2012. [pdf] [bibtex]

  • Y. Yu and D. Schuurmans, Rank/norm regularization with closed-form solutions: Application to subspace clustering. UAI 2011. [pdf] [bibtex]

  • X. Zhang, Y. Yu, M. White, R. Huang and D. Schuurmans, Convex sparse coding, subspace learning, and semi-supervised extensions. AAAI 2011. [pdf] [bibtex]

  • Y. Yu, J. Jiang and L. Zhang, Distance Metric Learning by Minimal Distance Maximization. Pattern Recognition, 2011, vol. 44: 639-649. [pdf] [bibtex]

  • Y. Yu, M. Yang, L. Xu, M. White and D. Schuurmans, Relaxed Clipping: A Global Training Method for Robust Regression and Classification. NIPS 2010. [pdf] [bibtex]

  • Y. Yu, Y. Li, D. Schuurmans and C. Szepesvári, A General Projection Property for Distribution Families. NIPS 2009. [pdf] [bibtex]