Assistant Professor
Department of Statistical Science
Faculty of Computing and Information Science
pingli [att] cornell [dot] edu
1192 Comstock Hall, (1 607) 255 9813
| Ping Li
Assistant Professor Department of Statistical Science Faculty of Computing and Information Science pingli [att] cornell [dot] edu 1192 Comstock Hall, (1 607) 255 9813 |
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2010 Prize in Yahoo! Learning to Rank Grand Challenge 2009 ONR (Office of Naval Research) Young Investigator Award 2006 SIGKDD Best Student Paper Award |
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Ph.D. in Statistics, Stanford University, 2007 |
| Machine Learning, Randomized Algorithms for Large Data Sets |
| Srping 2012 Categorical Data |
| Fall 2008, Fall 2009 Theory of Probability |
| Spring 2009, Spring 2010, Theory of Statistics |
| Spring 2008, Spring 2009, Statistical Computing |
| Spring 2011, Computationally Intensive Statistical Methods |
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Ping Li, Anshumali Shrivastava, Joshua Moore, and Christian Konig Hashing Algorithms for Large-Scale Learning, Neural Information Processing Systems (NIPS), 2011. |
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Ping Li and Christian Konig Theory and Applications of b-Bit Minwise Hashing, Research Hightlights, Communications of the ACM, 2011. (Previous version apeared in WWW 2010) |
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Ping Li and Cun-Hui Zhang A New Algorithm for Compressed Counting with Applications in Shannon Entropy Estimation in Dynamic Data, Conference on Learning Theory (COLT), 2011. |
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Ping Li, Christian Konig, and Wenhao Gui b-Bit Minwise Hashing for Estimating Three-Way Similarities, Neural Information Processing Systems (NIPS), 2010. |
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Ping Li, Robust LogitBoost and Adaptive Base Class (ABC) LogitBoost, Uncertainty on Artificial Intelligence (UAI), 2010. |
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Ping Li, Michael Mahoney, and Yiyuan She Estimating Higher-Order Distances Using Random Projections, Uncertainty on Artificial Intelligence (UAI), 2010. Ping Li, ABC-Boost: Adaptive Base Class Boost for Multi-class Classification, International Conference on Machine Learning (ICML), 2009. Ping Li, Compressed Counting, ACM-SIAM Symposium on Discrete Algorithms (SODA), 2009. Ping Li, Kenneth W. Church and Trevor J. Hastie One Sketch For All: Theory and Application of Conditional Random Sampling, Neural Information Processing Systems (NIPS), 2008 Ping Li, Chris Burges, and Qiang Wu McRank: Learning to Rank Using Multiple Classifications and Gradient Boosting, Neural Information Processing Systems (NIPS), 2007 Ping Li, Trevor J. Hastie, and Kenneth W. Church, Nonlinear Estimators and Tail Bounds for Dimension Reduction in L1 Using Cauchy Random Projections, Journal of Machine Learning Research (JMLR), 2497-2532, 2007. Ping Li and Kenneth W. Church, A sketch algorithm for estimating two-way and multi-way associations, Computational Linguistics 33(3), 305-354, 2007 Ping Li, Debashis Paul, Ravi Narasimhan and John Cioffi, On the distribution of SINR for the MMSE MIMO receiver and performance analysis, IEEE Transactions on Information Theory, 52:1, 271-286, 2006. |