| Patterns and rates of exonic de novo mutations in autism spectrum disorders BM Neale, Y Kou, L Liu, A Ma’ayan, KE Samocha, A Sabo, CF Lin, ... Nature 485, 242-245, 2012 | 2068 | 2012 |
| Challenges of big data analysis J Fan, F Han, H Liu National science review 1 (2), 293-314, 2014 | 1861 | 2014 |
| The nonparanormal: Semiparametric estimation of high dimensional undirected graphs H Liu, J Lafferty, L Wasserman The Journal of Machine Learning Research 10, 2295-2328, 2009 | 877 | 2009 |
| Sparse additive models P Ravikumar, J Lafferty, H Liu, L Wasserman Journal of the Royal Statistical Society: Series B 75 (5), 1009-1030, 2009 | 841 | 2009 |
| Fully decentralized multi-agent reinforcement learning with networked agents K Zhang, Z Yang, H Liu, T Zhang, T Basar International conference on machine learning, 5872-5881, 2018 | 689 | 2018 |
| High-dimensional semiparametric Gaussian copula graphical models H Liu, F Han, M Yuan, J Lafferty, L Wasserman | 640 | 2012 |
| DNABERT: pre-trained Bidirectional Encoder Representations from Transformers model for DNA-language in genome Y Ji, Z Zhou, H Liu, RV Davuluri Bioinformatics 37 (15), 2112-2120, 2021 | 586 | 2021 |
| Stability approach to regularization selection (stars) for high dimensional graphical models H Liu, K Roeder, L Wasserman Advances in Neural Information Processing Systems 23, 1432-1440, 2010 | 582 | 2010 |
| The huge package for high-dimensional undirected graph estimation in R T Zhao, H Liu, K Roeder, J Lafferty, L Wasserman The Journal of Machine Learning Research 13 (1), 1059-1062, 2012 | 558 | 2012 |
| An overview of the estimation of large covariance and precision matrices J Fan, Y Liao, H Liu The Econometrics Journal 19 (1), C1-C32, 2016 | 435 | 2016 |
| A general theory of hypothesis tests and confidence regions for sparse high dimensional models Y Ning, H Liu | 355 | 2017 |
| Stochastic compositional gradient descent: algorithms for minimizing compositions of expected-value functions M Wang, EX Fang, H Liu Mathematical Programming 161, 419-449, 2017 | 278 | 2017 |
| Blockwise coordinate descent procedures for the multi-task lasso, with applications to neural semantic basis discovery H Liu, M Palatucci, J Zhang Proceedings of the 26th annual international conference on machine learning …, 2009 | 229 | 2009 |
| Parametrized deep q-networks learning: Reinforcement learning with discrete-continuous hybrid action space J Xiong, Q Wang, Z Yang, P Sun, L Han, Y Zheng, H Fu, T Zhang, J Liu, ... arXiv preprint arXiv:1810.06394, 2018 | 217 | 2018 |
| Distributed testing and estimation under sparse high dimensional models H Battey, J Fan, H Liu, J Lu, Z Zhu Annals of statistics 46 (3), 1352, 2018 | 217 | 2018 |
| Optimal computational and statistical rates of convergence for sparse nonconvex learning problems Z Wang, H Liu, T Zhang Annals of statistics 42 (6), 2164, 2014 | 206 | 2014 |
| Few-shot slot tagging with collapsed dependency transfer and label-enhanced task-adaptive projection network Y Hou, W Che, Y Lai, Z Zhou, Y Liu, H Liu, T Liu arXiv preprint arXiv:2006.05702, 2020 | 200 | 2020 |
| A strictly contractive peaceman--rachford splitting method for convex programming B He, H Liu, Z Wang, X Yuan SIAM Journal on Optimization 24 (3), 1011-1040, 2014 | 195 | 2014 |
| A gut commensal bacterium promotes mosquito permissiveness to arboviruses P Wu, P Sun, K Nie, Y Zhu, M Shi, C Xiao, H Liu, Q Liu, T Zhao, X Chen, ... Cell host & microbe 25 (1), 101-112. e5, 2019 | 193 | 2019 |
| A nonconvex optimization framework for low rank matrix estimation T Zhao, Z Wang, H Liu Advances in Neural Information Processing Systems 28, 2015 | 171 | 2015 |
Larry WassermanProfessor, Department of Statistics and Machine Learning Department, Carnegie Mellon UniversityVerified email at stat.cmu.edu
