| Parallelizing MCMC via Weierstrass sampler X Wang, DB Dunson Joint Statistical Meetings 2014, 2014 | 113* | 2014 |
| High dimensional ordinary least squares projection for screening variables X Wang, C Leng Journal of the Royal Statistical Society: Series B (Statistical Methodology …, 2016 | 46 | 2016 |
| Parallelizing MCMC with random partition trees X Wang, F Guo, KA Heller, DB Dunson Advances in neural information processing systems, 451-459, 2015 | 44 | 2015 |
| Boosting variational inference F Guo, X Wang, K Fan, T Broderick, DB Dunson Advances in Approximate Bayesian Inference, NIPS 2016 Workshop, 2016 | 30 | 2016 |
| Median selection subset aggregation for parallel inference X Wang, P Peng, DB Dunson Advances in neural information processing systems, 2195-2203, 2014 | 21 | 2014 |
| DECOrrelated feature space partitioning for distributed sparse regression X Wang, DB Dunson, C Leng Advances in neural information processing systems, 802-810, 2016 | 17 | 2016 |
| Towards unifying Hamiltonian Monte Carlo and slice sampling Y Zhang, X Wang, C Chen, R Henao, K Fan, L Carin Advances in Neural Information Processing Systems, 1741-1749, 2016 | 14 | 2016 |
| Quda: A direct approach for sparse quadratic discriminant analysis B Jiang, X Wang, C Leng arXiv preprint arXiv:1510.00084, 2015 | 11* | 2015 |
| On the consistency theory of high dimensional variable screening X Wang, C Leng, DB Dunson Advances in Neural Information Processing Systems, 2431-2439, 2015 | 7 | 2015 |
| No penalty no tears: Least squares in high-dimensional linear models X Wang, D Dunson, C Leng International Conference on Machine Learning, 1814-1822, 2016 | 5 | 2016 |
| Boosting Variational Inference: Theory and Examples X Wang PhD thesis, Duke University, 2016 | 4 | 2016 |
| Distributed Feature Selection in Large n andLarge p Regression Problems X Wang Duke University, 2016 | | 2016 |
Chenlei Leng (冷琛雷)Professor of Statistics, University of WarwickVerifierad e-postadress på warwick.ac.uk