Publications and working papers
- Qihang Lin, Xi Chen and Javier Peņa, A Smoothing Stochastic Gradient Method for Composite Optimization, Optimization Methods and Software, 2014, accepted.
- Qihang Lin, Xi Chen and Javier Peņa, A Sparsity Preserving Stochastic Gradient Method for Composite Optimization, Computational Optimization and Applications, 2014, accepted.
- Qihang Lin and Lin Xiao, An Accelerated Proximal-Gradient Homotopy Method for the Sparse Least-Squares Problem, International Conference on Machine Learning (ICML) 2014.
- Xi Chen, Qihang Lin and Dengyong Zhou, Optimistic Knowledge Gradient Policy for Optimal Budget Allocation in Crowdsourcing, International Conference on Machine Learning (ICML) 2013.
- Xi Chen, Qihang Lin and Javier Peņa, Optimal Regularized Dual Averaging Methods for Stochastic Optimization, Advances in Neural Information Processing Systems (NIPS) 2012.
- Xi Chen, Qihang Lin, Seyoung Kim, Jaime G. Carbonell and Eric P. Xing, Smoothing Proximal Gradient Method for General Structured Sparse Learning, Annals of Applied Statistics, Volume 6, Number 2 (2012), 719-752. The conference version is accepted by Uncertainty in Artificial Intelligence (UAI), 2011. The code for this paper is available for download.
- Xi Chen, Yanjun Qi, Bing Bai, Qihang Lin and Jaime Carbonell. Sparse Latent Semantic Analysis, SIAM International Conference on Data Mining (SDM), 2011.
- Xi Chen, Yanjun Qi, Bing Bai, Qihang Lin and Jaime Carbonell. Learning Preferences using Millions of Parameters by Enforcing Sparsity, International Conference on Data Mining (ICDM), 2010.
- Qihang Lin and Javier Peņa, Optimal Trade Execution with Coherent Dynamic Risk Measures, under review. (This paper wins the First Place of INFORMS Financial Service Section Best Student Research Paper Award in 2012.)
- Qihang Lin and Javier Peņa, An Interior-Point Algorithm for Computing the Nash Equilibrium of Two-Person Zero-Sum Sequential Games, technical report.
- Qihang Lin, Alan L. Montgomery and Kinshuk Jerath. Predicting Purchase Conversion Rates for Online Search Advertisement using Text Mining, in preparation.
please send me an email for papers which are not available for donwload.