| Grouped graphical Granger modeling for gene expression regulatory networks discovery AC Lozano, N Abe, Y Liu, S Rosset Bioinformatics 25 (12), i110-i118, 2009 | 209 | 2009 |
| Protein representation learning by geometric structure pretraining Z Zhang, M Xu, A Jamasb, V Chenthamarakshan, A Lozano, P Das, ... arXiv preprint arXiv:2203.06125, 2022 | 196 | 2022 |
| Stratification of TAD boundaries reveals preferential insulation of super-enhancers by strong boundaries Y Gong, C Lazaris, T Sakellaropoulos, A Lozano, P Kambadur, ... Nature communications 9 (1), 542, 2018 | 151 | 2018 |
| Spatial-temporal causal modeling for climate change attribution AC Lozano, H Li, A Niculescu-Mizil, Y Liu, C Perlich, J Hosking, N Abe Proceedings of the 15th ACM SIGKDD international conference on Knowledge …, 2009 | 146 | 2009 |
| Grouped orthogonal matching pursuit for variable selection and prediction G Swirszcz, N Abe, AC Lozano Advances in Neural Information Processing Systems 22, 2009 | 133 | 2009 |
| Generalized Kalman smoothing: Modeling and algorithms A Aravkin, JV Burke, L Ljung, A Lozano, G Pillonetto Automatica 86, 63-86, 2017 | 127 | 2017 |
| Multi-level lasso for sparse multi-task regression AC Lozano, G Swirszcz Proceedings of the 29th International Coference on International Conference …, 2012 | 111* | 2012 |
| Proximity-based anomaly detection using sparse structure learning T Idé, AC Lozano, N Abe, Y Liu Proceedings of the 2009 SIAM international conference on data mining, 97-108, 2009 | 93 | 2009 |
| Casual modeling of multi-dimensional hierarchical metric cubes N Abe, J Fu, MG Gemmell, S Kapoor, FS Kelly, DM Loehr, AC Lozano, ... US Patent 10,360,527, 2019 | 82 | 2019 |
| Group orthogonal matching pursuit for logistic regression A Lozano, G Swirszcz, N Abe Proceedings of the fourteenth international conference on artificial …, 2011 | 73 | 2011 |
| Learning temporal causal graphs for relational time-series analysis Y Liu, A Niculescu-Mizil, AC Lozano, Y Lu Proceedings of the 27th International Conference on Machine Learning (ICML …, 2010 | 72 | 2010 |
| Grouped graphical Granger modeling methods for temporal causal modeling AC Lozano, N Abe, Y Liu, S Rosset Proceedings of the 15th ACM SIGKDD international conference on Knowledge …, 2009 | 62 | 2009 |
| Scalable matrix-valued kernel learning for high-dimensional nonlinear multivariate regression and granger causality V Sindhwani, MH Quang, AC Lozano arXiv preprint arXiv:1210.4792, 2012 | 58 | 2012 |
| Convergence and consistency of regularized boosting algorithms with stationary b-mixing observations AC Lozano, S Kulkarni, RE Schapire Advances in neural information processing systems 18, 2005 | 56 | 2005 |
| Methods and systems for variable group selection and temporal causal modeling N Abe, Y Liu, AC Lozano, S Rosset, G Swirszcz US Patent 8,255,346, 2012 | 54 | 2012 |
| Multi-class cost-sensitive boosting with p-norm loss functions AC Lozano, N Abe Proceedings of the 14th ACM SIGKDD international conference on Knowledge …, 2008 | 51 | 2008 |
| Bayesian regularization via graph Laplacian F Liu, S Chakraborty, F Li, Y Liu, AC Lozano | 42 | 2014 |
| Elementary estimators for high-dimensional linear regression E Yang, A Lozano, P Ravikumar International Conference on Machine Learning, 388-396, 2014 | 40 | 2014 |
| A general family of trimmed estimators for robust high-dimensional data analysis E Yang, AC Lozano, A Aravkin | 37 | 2018 |
| Elementary estimators for graphical models E Yang, AC Lozano, PK Ravikumar Advances in neural information processing systems 27, 2014 | 36 | 2014 |
Yan LiuProfessor, Director of Machine Learning Center@USC在 usc.edu 的电子邮件经过验证