| Parallelizing MCMC via Weierstrass sampler X Wang, DB Dunson Joint Statistical Meetings 2014, 2014 | 153* | 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 | 76 | 2016 |
| Parallelizing MCMC with random partition trees X Wang, F Guo, KA Heller, DB Dunson arXiv preprint arXiv:1506.03164, 2015 | 56 | 2015 |
| Boosting variational inference F Guo, X Wang, K Fan, T Broderick, DB Dunson Advances in Approximate Bayesian Inference, NIPS 2016 Workshop, 2016 | 43 | 2016 |
| Median selection subset aggregation for parallel inference X Wang, P Peng, D Dunson arXiv preprint arXiv:1410.6604, 2014 | 25 | 2014 |
| A direct approach for sparse quadratic discriminant analysis B Jiang, X Wang, C Leng The Journal of Machine Learning Research 19 (1), 1098-1134, 2018 | 22* | 2018 |
| DECOrrelated feature space partitioning for distributed sparse regression X Wang, D Dunson, C Leng arXiv preprint arXiv:1602.02575, 2016 | 19 | 2016 |
| Towards unifying Hamiltonian Monte Carlo and slice sampling Y Zhang, X Wang, C Chen, R Henao, K Fan, L Carin arXiv preprint arXiv:1602.07800, 2016 | 18 | 2016 |
| 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 | 11 | 2016 |
| Boosting variational inference: theory and examples X Wang Master’s thesis, Duke University, 2016 | 8 | 2016 |
| On the consistency theory of high dimensional variable screening X Wang, C Leng, DB Dunson arXiv preprint arXiv:1502.06895, 2015 | 7 | 2015 |
| Distributed Feature Selection in Large n and Large 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
