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\n  \n 2007\n \n \n (1)\n \n \n
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\n \n\n \n \n \n \n \n \n A Novel Facial Feature Point Localization Method on 3D Faces.\n \n \n \n \n\n\n \n Guan, P.; Yu, Y.; and Zhang, L.\n\n\n \n\n\n\n In IEEE Conference on Image Processing (ICIP), 2007. \n \n\n\n\n
\n\n\n\n \n \n \"A paper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n\n\n\n
\n 
@inproceedings{GuanYZ07,\n  title \t    = {A Novel Facial Feature Point Localization Method on 3D Faces},\n  author \t    = {P. Guan and Y. Yu and L. Zhang},\n  booktitle   = {{IEEE} Conference on Image Processing ({ICIP})},\n  year \t\t    = {2007},\n  url_Paper   = {http://www.cs.cmu.edu/~yaoliang/mypapers/icip07.pdf},  \n  keyword     = {2007},\n}\t\n\n\n\n%\\item \\textit{A Conditional Value-at-Risk Approach for Uncertain Markov Decision Processes}\\\\[0.5ex]\n%\\small{\\textbf{Yaoliang Yu}, Csaba Szepesv\\'ari, Yuxi Li, and Dale Schuurmans}\\\\[0.5ex]\n%Multidisciplinary Symposium on Reinforcement Learning, 2009\n%\n%\\item \\textit{Online TD(1) Meets Offline Monte Carlo}\\\\[0.5ex]\n%\\small{\\textbf{Yaoliang Yu}, Yongjian Zhang, and Csaba Szepesv\\'ari}\\\\[0.5ex]\n%Multidisciplinary Symposium on Reinforcement Learning, 2009\n\n\n
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\n  \n 2009\n \n \n (1)\n \n \n
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\n \n\n \n \n \n \n \n \n A General Projection Property for Distribution Families.\n \n \n \n \n\n\n \n Yu, Y.; Li, Y.; Schuurmans, D.; and Szepesvári, C.\n\n\n \n\n\n\n In Advances in Neural Information Processing Systems (NIPS), 2009. \n \n\n\n\n
\n\n\n\n \n \n \"A paper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n\n\n\n
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@inproceedings{YuLSS09,\n  title \t    = {A General Projection Property for Distribution Families},\n  author \t    = {Yao-Liang Yu and Y. Li and D. Schuurmans and C. Szepesv\\'ari},\n  booktitle   = {Advances in Neural Information Processing Systems ({NIPS})},\n  year \t\t    = {2009},\n  url_Paper   = {http://www.cs.cmu.edu/~yaoliang/mypapers/nips09.pdf},    \n  keyword     = {2009},\n}\t\n\n
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\n  \n 2010\n \n \n (1)\n \n \n
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\n \n\n \n \n \n \n \n \n Relaxed Clipping: A Global Training Method for Robust Regression and Classification.\n \n \n \n \n\n\n \n Yu, Y.; Yang, M.; Xu, L.; White, M.; and Schuurmans, D.\n\n\n \n\n\n\n In Advances in Neural Information Processing Systems (NIPS), 2010. \n \n\n\n\n
\n\n\n\n \n \n \"Relaxed paper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n\n\n\n
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@inproceedings{YuYXWS10,\n  title \t    = {Relaxed Clipping: A Global Training Method for Robust Regression and Classification},\n  author \t    = {Y. Yu and M. Yang and L. Xu and M. White and D. Schuurmans},\n  booktitle   = {Advances in Neural Information Processing Systems ({NIPS})},\n  year \t\t    = {2010},\n  url_Paper   = {http://www.cs.cmu.edu/~yaoliang/mypapers/nips10.pdf},\n  keyword     = {2010},  \n}\n\n
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\n  \n 2011\n \n \n (3)\n \n \n
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\n \n\n \n \n \n \n \n \n Rank/Norm Regularization with Closed-Form Solutions: Application to Subspace Clustering.\n \n \n \n \n\n\n \n Yu, Y.; and Schuurmans, D.\n\n\n \n\n\n\n In Conference on Uncertainty in Artificial Intelligence (UAI), 2011. \n \n\n\n\n
\n\n\n\n \n \n \"Rank/Norm paper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n\n\n\n
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@inproceedings{YuSchuurmans11,\n  title \t    = {Rank/Norm Regularization with Closed-Form Solutions: Application to Subspace Clustering},\n  author \t    = {Yao-Liang Yu and D. Schuurmans},\n  booktitle   = {Conference on Uncertainty in Artificial Intelligence ({UAI})},\n  year \t\t    = {2011},\n  url_Paper   = {http://www.cs.cmu.edu/~yaoliang/mypapers/uai11.pdf},\n  keyword     = {2011},  \n}\n\n
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\n \n\n \n \n \n \n \n \n Convex Sparse Coding, Subspace Learning, and Semi-Supervised Extensions.\n \n \n \n \n\n\n \n Zhang, X.; Yu, Y.; White, M.; Huang, R.; and Schuurmans, D.\n\n\n \n\n\n\n In Association for the Advancement of Artificial Intelligence (AAAI), 2011. \n \n\n\n\n
\n\n\n\n \n \n \"Convex paper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n\n\n\n
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@inproceedings{ZhangYWHS11,\n  title       = {Convex Sparse Coding, Subspace Learning, and Semi-Supervised Extensions},\n  author      = {X. Zhang and Y. Yu and M. White and R. Huang and D. Schuurmans},\n  booktitle   = {Association for the Advancement of Artificial Intelligence {(AAAI)}},\n  year        = {2011},\n  url_Paper   = {http://www.cs.cmu.edu/~yaoliang/mypapers/aaai11.pdf},  \n  keyword     = {2011},  \n}\n\n
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\n \n\n \n \n \n \n \n \n Distance Metric Learning by Minimal Distance Maximization.\n \n \n \n \n\n\n \n Yu, Y.; Jiang, J.; and Zhang, L.\n\n\n \n\n\n\n Pattern Recognition, 44: 639–649. 2011.\n \n\n\n\n
\n\n\n\n \n \n \"Distance paper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n\n\n\n
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@article{YuJZ11,\n  title\t\t    = {Distance Metric Learning by Minimal Distance Maximization},\n  author      = {Yao-Liang Yu and J. Jiang and L. Zhang},  \n  journal     = {Pattern Recognition},\n  volume\t    = {44},\n  pages\t\t    = {639--649},\n  year\t\t    = {2011},\n  url_Paper   = {http://www.cs.cmu.edu/~yaoliang/mypapers/pr11.pdf},  \n  keyword     = {2011},  \n}\t\n\n
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\n  \n 2012\n \n \n (5)\n \n \n
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\n \n\n \n \n \n \n \n \n Accelerated Training for Matrix-Norm Regularization: A Boosting Approach.\n \n \n \n \n\n\n \n Zhang, X.; Yu, Y.; and Schuurmans, D.\n\n\n \n\n\n\n In Advances in Neural Information Processing Systems (NIPS), 2012. \n \n\n\n\n
\n\n\n\n \n \n \"Accelerated paper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n\n\n\n
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@inproceedings{ZhangYS12,\n  title \t    = {Accelerated Training for Matrix-Norm Regularization: A Boosting Approach},\n  author \t    = {X. Zhang and Y. Yu and D. Schuurmans},\n  booktitle   = {Advances in Neural Information Processing Systems ({NIPS})},\n  year \t\t    = {2012},\n  url_Paper   = {http://www.cs.cmu.edu/~yaoliang/mypapers/nips12c.pdf},  \n  keyword     = {2012},  \n}\n\n
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\n \n\n \n \n \n \n \n \n Convex Multi-view Subspace Learning.\n \n \n \n \n\n\n \n White, M.; Yu, Y.; Zhang, X.; and Schuurmans, D.\n\n\n \n\n\n\n In Advances in Neural Information Processing Systems (NIPS), 2012. \n \n\n\n\n
\n\n\n\n \n \n \"Convex paper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n\n\n\n
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@inproceedings{WhiteYZS12,\n  title \t    = {Convex Multi-view Subspace Learning},\n  author \t    = {M. White and Y. Yu and X. Zhang and D. Schuurmans},\n  booktitle   = {Advances in Neural Information Processing Systems ({NIPS})},\n  year \t\t    = {2012},\n  url_Paper   = {http://www.cs.cmu.edu/~yaoliang/mypapers/nips12b.pdf},  \n  keyword     = {2012},  \n}\n\n
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\n \n\n \n \n \n \n \n \n A Polynomial-time Form of Robust Regression.\n \n \n \n \n\n\n \n Yu, Y.; Aslan, Ö.; and Schuurmans, D.\n\n\n \n\n\n\n In Advances in Neural Information Processing Systems (NIPS), 2012. \n \n\n\n\n
\n\n\n\n \n \n \"A paper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n\n\n\n
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@inproceedings{YuAS12,\n  title \t    = {A Polynomial-time Form of Robust Regression},\n  author \t    = {Y. Yu and \\"O. Aslan and D. Schuurmans},\n  booktitle   = {Advances in Neural Information Processing Systems ({NIPS})},\n  year \t\t    = {2012},\n  url_Paper   = {http://www.cs.cmu.edu/~yaoliang/mypapers/nips12a.pdf},  \n  keyword     = {2012},  \n}\n\n
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\n \n\n \n \n \n \n \n \n Regularizers versus Losses for Nonlinear Dimensionality Reduction.\n \n \n \n \n\n\n \n Yu, Y.; Neufeld, J.; Kiros, R.; Zhang, X.; and Schuurmans, D.\n\n\n \n\n\n\n In International Conference on Machine Learning (ICML), 2012. \n \n\n\n\n
\n\n\n\n \n \n \"Regularizers paper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n\n\n\n
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@inproceedings{YuNKZS12,\n  title       = {Regularizers versus Losses for Nonlinear Dimensionality Reduction},\n  author \t    = {Y. Yu and J. Neufeld and R. Kiros and X. Zhang and D. Schuurmans},\n  booktitle   = {International Conference on Machine Learning ({ICML})},\n  year \t\t    = {2012},\n  url_Paper   = {http://www.cs.cmu.edu/~yaoliang/mypapers/icml12b.pdf},   \n  keyword     = {2012},  \n}\n\n
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\n \n\n \n \n \n \n \n \n Analysis of Kernel Mean Matching under Covariate Shift.\n \n \n \n \n\n\n \n Yu, Y.; and Szepesvári, C.\n\n\n \n\n\n\n In International Conference on Machine Learning (ICML), 2012. \n \n\n\n\n
\n\n\n\n \n \n \"Analysis paper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n\n\n\n
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@inproceedings{YuSzepesvari12,\n  title \t    = {Analysis of Kernel Mean Matching under Covariate Shift},\n  author \t    = {Yao-Liang Yu and C. Szepesv\\'ari},\n  booktitle   = {International Conference on Machine Learning ({ICML})},\n  year \t\t    = {2012},\n  url_Paper   = {http://www.cs.cmu.edu/~yaoliang/mypapers/icml12a.pdf},  \n  keyword     = {2012},  \n}\n\n
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\n  \n 2013\n \n \n (4)\n \n \n
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\n \n\n \n \n \n \n \n \n Better Approximation and Faster Algorithm Using the Proximal Average.\n \n \n \n \n\n\n \n Yu, Y.\n\n\n \n\n\n\n In Advances in Neural Information Processing Systems (NIPS), 2013. \n \n\n\n\n
\n\n\n\n \n \n \"Better paper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n\n\n\n
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@inproceedings{Yu13b,\n  title       = {Better Approximation and Faster Algorithm Using the Proximal Average},\n  author      = {Yao-Liang Yu},\n  booktitle   = {Advances in Neural Information Processing Systems ({NIPS})},\n  year \t\t    = {2013},\n  url_Paper   = {http://www.cs.cmu.edu/~yaoliang/mypapers/nips13b.pdf},  \n  keyword     = {2013},  \n}\n\n
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\n \n\n \n \n \n \n \n \n On Decomposing the Proximal Map.\n \n \n \n \n\n\n \n Yu, Y.\n\n\n \n\n\n\n In Advances in Neural Information Processing Systems (NIPS), 2013. \n \n\n\n\n
\n\n\n\n \n \n \"On paper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n\n\n\n
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@inproceedings{Yu13a,\n  title       = {On Decomposing the Proximal Map},\n  author      = {Yao-Liang Yu},\n  booktitle   = {Advances in Neural Information Processing Systems ({NIPS})},\n  year \t\t    = {2013},\n  url_Paper   = {http://www.cs.cmu.edu/~yaoliang/mypapers/nips13a.pdf},  \n  keyword     = {2013},\n}\n\n
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\n \n\n \n \n \n \n \n \n Polar Operators for Structured Sparse Estimation.\n \n \n \n \n\n\n \n Zhang, X.; Yu, Y.; and Schuurmans, D.\n\n\n \n\n\n\n In Advances in Neural Information Processing Systems (NIPS), 2013. \n \n\n\n\n
\n\n\n\n \n \n \"Polar paper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n\n\n\n
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@inproceedings{ZhangYS13,\n  title       = {Polar Operators for Structured Sparse Estimation},\n  author      = {X. Zhang and Y. Yu and D. Schuurmans},\n  booktitle   = {Advances in Neural Information Processing Systems ({NIPS})},\n  year \t\t    = {2013},\n  url_Paper   = {http://www.cs.cmu.edu/~yaoliang/mypapers/nips13c.pdf},  \n  keyword     = {2013},  \n}\n\n
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\n \n\n \n \n \n \n \n \n Characterizing the Representer Theorem.\n \n \n \n \n\n\n \n Yu, Y.; Cheng, H.; Schuurmans, D.; and Szepesvári, C.\n\n\n \n\n\n\n In International Conference on Machine Learning (ICML), 2013. \n \n\n\n\n
\n\n\n\n \n \n \"Characterizing paper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n\n\n\n
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@inproceedings{YuCSS13,\n  title       = {Characterizing the Representer Theorem},\n  author      = {Yao-Liang Yu and H. Cheng and D. Schuurmans and C. Szepesv\\'ari},\n  booktitle   = {International Conference on Machine Learning ({ICML})},\n  year \t\t    = {2013},\n  url_Paper   = {http://www.cs.cmu.edu/~yaoliang/mypapers/icml13.pdf},  \n  keyword     = {2013},  \n}\n\n
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\n  \n 2014\n \n \n (1)\n \n \n
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\n \n\n \n \n \n \n \n \n Efficient Structured Matrix Rank Minimization.\n \n \n \n \n\n\n \n Yu, A.; Ma, W.; Yu, Y.; Carbonell, J.; and Sra, S.\n\n\n \n\n\n\n In Advances in Neural Information Processing Systems (NIPS), 2014. \n \n\n\n\n
\n\n\n\n \n \n \"Efficient paper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n\n\n\n
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@inproceedings{YuMYCS14,\n  title       = {Efficient Structured Matrix Rank Minimization},\n  author      = {A. Yu and W. Ma and Y. Yu and J. Carbonell and S. Sra},\n  booktitle   = {Advances in Neural Information Processing Systems {(NIPS)}},  \n  year        = {2014},\n  url_Paper   = {http://www.cs.cmu.edu/~yaoliang/mypapers/nips14.pdf},  \n  keyword     = {2014},  \n}  \n\n
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\n  \n 2015\n \n \n (6)\n \n \n
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\n \n\n \n \n \n \n \n \n Petuum: A New Platform for Distributed Machine Learning on Big Data.\n \n \n \n \n\n\n \n Xing, E.; Ho, Q.; Dai, W.; Kim, J.; Wei, J.; Lee, S.; Zheng, X.; Xie, P.; Kumar, A.; and Yu, Y.\n\n\n \n\n\n\n IEEE Transactions on Big Data, 1(2): 49–67. 2015.\n \n\n\n\n
\n\n\n\n \n \n \"Petuum: paper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n\n\n\n
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@article{Xingetal15, \n  title       = {Petuum: A New Platform for Distributed Machine Learning on Big Data},\n  author      = {E. Xing and Ho, Q. and Dai, W. and Kim, J. and Wei, J. and Lee, S. and Zheng, X. and Xie, P. and Kumar, A. and Y. Yu},\n  journal     = {{IEEE} Transactions on Big Data}, \n  volume      = {1},\n  number      = {2},\n  pages       = {49--67},\n  year        = {2015}, \n  addendum    = {(Preliminary version appeared in KDD, 2015)},\n  url_Paper   = {http://www.cs.cmu.edu/~yaoliang/mypapers/petuum.pdf},  \n  keyword     = {2015},  \n}\n\n
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\n \n\n \n \n \n \n \n \n Searching Persuasively: Joint Event Detection and Evidence Recounting with Limited Supervision.\n \n \n \n \n\n\n \n Chang, X.; Yu, Y.; Yang, Y.; and Hauptmann, A.\n\n\n \n\n\n\n In ACM Conference on Multimedia (MM), 2015. \n \n\n\n\n
\n\n\n\n \n \n \"Searching paper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n\n\n\n
\n 
@inproceedings{ChangYYH15,\n  title       = {Searching Persuasively: Joint Event Detection and Evidence Recounting with Limited Supervision},\n  author      = {X. Chang and Yao-Liang Yu and Y. Yang and A. Hauptmann},\n  year        = {2015},\n  booktitle   = {{ACM} Conference on Multimedia ({MM})},\n  url_Paper   = {http://www.cs.cmu.edu/~yaoliang/mypapers/mm15.pdf},  \n  keyword     = {2015},\n}\n\n
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\n \n\n \n \n \n \n \n \n Linear Time Samplers for Supervised Topic Models using Compositional Proposals.\n \n \n \n \n\n\n \n Zheng, X.; Yu, Y.; and Xing, E.\n\n\n \n\n\n\n In ACM Conference on Knowledge Discovery and Data Mining (KDD), 2015. \n \n\n\n\n
\n\n\n\n \n \n \"Linear paper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n\n\n\n
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@inproceedings{ZhengYX15,\n  title       = {Linear Time Samplers for Supervised Topic Models using Compositional Proposals},\n  author      = {X. Zheng and Y. Yu and E. Xing},\n  year        = {2015},\n  booktitle   = {ACM Conference on Knowledge Discovery and Data Mining {(KDD)}},\n  url_Paper   = {http://www.cs.cmu.edu/~yaoliang/mypapers/kdd15.pdf},  \n  keyword     = {2015},  \n}\n\n
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\n \n\n \n \n \n \n \n \n Semantic Concept Discovery for Large-Scale Zero-Shot Event Detection.\n \n \n \n \n\n\n \n Chang, X.; Yang, Y.; Hauptmann, A.; Xing, E.; and Yu, Y.\n\n\n \n\n\n\n In International Joint Conference on Artificial Intelligence (IJCAI), 2015. \n \n\n\n\n
\n\n\n\n \n \n \"Semantic paper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n\n\n\n
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@inproceedings{ChangYHXY15,\n  title       = {Semantic Concept Discovery for Large-Scale Zero-Shot Event Detection},\n  author      = {X. Chang and Y. Yang and A. Hauptmann and E. Xing and Yao-Liang Yu},\n  year        = {2015},\n  booktitle   = {International Joint Conference on Artificial Intelligence {(IJCAI)}},\n  url_Paper   = {http://www.cs.cmu.edu/~yaoliang/mypapers/ijcai15.pdf},  \n  keyword     = {2015},  \n}\n\n
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\n \n\n \n \n \n \n \n \n Complex Event Detection using Semantic Saliency and Nearly-Isotonic SVM.\n \n \n \n \n\n\n \n Chang, X.; Yang, Y.; Xing, E.; and Yu, Y.\n\n\n \n\n\n\n In International Conference on Machine Learning (ICML), 2015. \n \n\n\n\n
\n\n\n\n \n \n \"Complex paper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n\n\n\n
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@inproceedings{ChangYXY15,\n  title       = {Complex Event Detection using Semantic Saliency and Nearly-Isotonic SVM},\n  author      = {X. Chang and Y. Yang and E. Xing and Yao-Liang Yu},\n  year        = {2015},\n  url_Paper   = {http://www.cs.cmu.edu/~yaoliang/mypapers/icml15.pdf},  \n  booktitle   = {International Conference on Machine Learning {(ICML)}},\n  keyword     = {2015},  \n}\n\n
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\n \n\n \n \n \n \n \n \n Minimizing Nonconvex Non-Separable Functions.\n \n \n \n \n\n\n \n Yu, Y.; Zheng, X.; Marchetti-Bowick, M.; and Xing, E.\n\n\n \n\n\n\n In International Conference on Artificial Intelligence and Statistics (AISTATS), 2015. \n \n\n\n\n
\n\n\n\n \n \n \"Minimizing paper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n\n\n\n
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@inproceedings{YuZMX15,\n  title       = {Minimizing Nonconvex Non-Separable Functions},\n  author      = {Yao-Liang Yu and X. Zheng and M. Marchetti-Bowick and E. Xing},\n  booktitle   = {International Conference on Artificial Intelligence and Statistics {(AISTATS)}},\n  year        = {2015},\n  url_Paper   = {http://www.cs.cmu.edu/~yaoliang/mypapers/aistats15.pdf},  \n  keyword     = {2015},  \n}\n\n\n
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\n  \n 2016\n \n \n (4)\n \n \n
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\n \n\n \n \n \n \n \n Not Equally Reliable: Semantic Event Search using Differentiated Concept Classifiers.\n \n \n \n\n\n \n Chang, X.; Yu, Y.; Yang, Y.; and Xing, E.\n\n\n \n\n\n\n In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016. \n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n\n\n\n
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@inproceedings{ChangYYX16,\n  title       = {Not Equally Reliable: Semantic Event Search using Differentiated Concept Classifiers},\n  author      = {X. Chang and Yao-Liang Yu and Y. Yang and E. Xing},\n  booktitle   = {{IEEE} Conference on Computer Vision and Pattern Recognition {(CVPR)}},\n  year        = {2016},\n  keyword     = {2016},  \n}\n\n
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\n \n\n \n \n \n \n \n Closed-Form Training of Mahalanobis Metric for Supervised Clustering.\n \n \n \n\n\n \n Law, M.; Yu, Y.; Cord, M.; and Xing, E.\n\n\n \n\n\n\n In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016. \n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n\n\n\n
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@inproceedings{LawYCX16,\n  title       = {Closed-Form Training of Mahalanobis Metric for Supervised Clustering},\n  author      = {M. Law and Y. Yu and M. Cord and E. Xing},\n  booktitle   = {{IEEE} Conference on Computer Vision and Pattern Recognition {(CVPR)}},\n  year        = {2016},\n  keyword     = {2016},  \n}\n
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\n \n\n \n \n \n \n \n Scalable and Sound Low-Rank Tensor Learning.\n \n \n \n\n\n \n Cheng, H.; Yu, Y.; Zhang, X.; Xing, E. P.; and Schuurmans, D.\n\n\n \n\n\n\n In International Conference on Artificial Intelligence and Statistics (AISTATS), 2016. \n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n\n\n\n
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@inproceedings{ChengYZXS16,\n  title       = {Scalable and Sound Low-Rank Tensor Learning},\n  author      = {Hao Cheng and Yaoliang Yu and Xinhua Zhang and Eric P. Xing and Dale Schuurmans},  \n  booktitle   = {International Conference on Artificial Intelligence and Statistics {(AISTATS)}},  \n  year        = {2016},\n  keyword     = {2016},    \n}  \n
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\n \n\n \n \n \n \n \n On Convergence of Model Parallel Proximal Gradient Algorithm for Stale Synchronous Parallel System.\n \n \n \n\n\n \n Zhou, Y.; Yu, Y.; Dai, W.; Liang, Y.; and Xing, E.\n\n\n \n\n\n\n In International Conference on Artificial Intelligence and Statistics (AISTATS), 2016. \n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n\n\n\n
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@inproceedings{ZhouYDLX16,\n  title       = {On Convergence of Model Parallel Proximal Gradient Algorithm for Stale Synchronous Parallel System},\n  author      = {Y. Zhou and Y. Yu and W. Dai and Y. Liang and E. Xing},\n  booktitle   = {International Conference on Artificial Intelligence and Statistics {(AISTATS)}},  \n  year        = {2016},\n  keyword     = {2016},  \n}\n
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\n  \n preprint\n \n \n (3)\n \n \n
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\n \n\n \n \n \n \n \n Additive Approximations in High Dimensional Nonparametric Regression via the SALSA.\n \n \n \n\n\n \n Kandasamy, K.; and Yu, Y.\n\n\n \n\n\n\n 2015.\n Submitted\n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n\n\n\n
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@unpublished{KandasamyYu15,\n  title       = {Additive Approximations in High Dimensional Nonparametric Regression via the {SALSA}},\n  author      = {K. Kandasamy and Y. Yu},\n  note        = {Submitted},\n  year        = {2015},\n  keyword    = {preprint},  \n}\n\n%@unpublished{Xieetal15,\n%  title       = {Distributed Machine Learning via Sufficient %Factor Broadcasting},\n%  author      = {P. Xie and J. Kim and Y. Zhou and Q. Ho and A. Kumar and Y. Yu and E. Xing},\n%  note        = {Submitted},\n%  year        = {2015},\n%  url_arXiv   = {http://arxiv.org/abs/1409.5705},    \n%  keyword    = {preprint},  \n% }\n\n
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\n \n\n \n \n \n \n \n Semantic Pooling for Untrimmed Video Analysis.\n \n \n \n\n\n \n Chang, X.; Yu, Y.; Yang, Y.; and Xing, E.\n\n\n \n\n\n\n Nov 2015.\n Submitted\n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n\n\n\n
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@unpublished{ChangYYX15c,\n  title       = {Semantic Pooling for Untrimmed Video Analysis},\n  author      = {X. Chang and Yao-Liang Yu and Y. Yang and E. Xing},\n  note        = {Submitted},\n  year        = {2015},\n  month       = {Nov},\n  addendum    = {Preliminary version appeared in ICML, 2015},\n  keyword    = {preprint},  \n}\n
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\n \n\n \n \n \n \n \n \n Generalized Conditional Gradient for Sparse Estimation.\n \n \n \n \n\n\n \n Yu, Y.; Zhang, X.; and Schuurmans, D.\n\n\n \n\n\n\n 2014.\n Submitted\n\n\n\n
\n\n\n\n \n \n \"Generalized paper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n\n\n\n
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@unpublished{YuZS14,\n  title       = {Generalized Conditional Gradient for Sparse Estimation},\n  author      = {Y. Yu and X. Zhang and D. Schuurmans},\n  note        = {Submitted},\n  year        = {2014},\n  url_Paper   = {http://www.cs.cmu.edu/~yaoliang/mypapers/gcg.pdf},  \n  keyword     = {preprint},  \n}\n\n
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