| DAGs with NO TEARS: Continuous optimization for structure learning X Zheng, B Aragam, PK Ravikumar, EP Xing Advances in Neural Information Processing Systems 31, 9472-9483, 2018 | 869 | 2018 |
| Learning Sparse Nonparametric DAGs X Zheng, C Dan, B Aragam, P Ravikumar, E Xing International Conference on Artificial Intelligence and Statistics, 3414-3425, 2020 | 257 | 2020 |
| DYNOTEARS: Structure Learning from Time-Series Data R Pamfil, N Sriwattanaworachai, S Desai, P Pilgerstorfer, K Georgatzis, ... International Conference on Artificial Intelligence and Statistics, 1595-1605, 2020 | 170 | 2020 |
| Precision Lasso: accounting for correlations and linear dependencies in high-dimensional genomic data H Wang, BJ Lengerich, B Aragam, EP Xing Bioinformatics 35 (7), 1181-1187, 2019 | 141 | 2019 |
| Concave penalized estimation of sparse Gaussian Bayesian networks B Aragam, Q Zhou Journal of Machine Learning Research 16, 2273-2328, 2015 | 125 | 2015 |
| Learning Large-Scale Bayesian Networks with the sparsebn Package B Aragam, J Gu, Q Zhou Journal of Statistical Software 91 (11), 2019 | 71 | 2019 |
| Identifiability of deep generative models without auxiliary information B Kivva, G Rajendran, P Ravikumar, B Aragam Advances in Neural Information Processing Systems 35, 2022 | 56* | 2022 |
| DAGMA: Learning DAGs via M-matrices and a Log-Determinant Acyclicity Characterization K Bello, B Aragam, P Ravikumar Advances in Neural Information Processing Systems 35, 2022 | 48 | 2022 |
| Learning latent causal graphs via mixture oracles B Kivva, G Rajendran, P Ravikumar, B Aragam Advances in Neural Information Processing Systems 34, 18087-18101, 2021 | 46 | 2021 |
| Fault Tolerance in Iterative-Convergent Machine Learning A Qiao, B Aragam, B Zhang, EP Xing International Conference on Machine Learning, 5220-5230, 2019 | 42 | 2019 |
| Identifiability of nonparametric mixture models and Bayes optimal clustering B Aragam, C Dan, EP Xing, P Ravikumar Annals of Statistics 48 (4), 2277-2302, 2020 | 38 | 2020 |
| A polynomial-time algorithm for learning nonparametric causal graphs M Gao, Y Ding, B Aragam Advances in Neural Information Processing Systems 33, 11599-11611, 2020 | 37 | 2020 |
| Learning directed acyclic graphs with penalized neighbourhood regression B Aragam, AA Amini, Q Zhou arXiv preprint arXiv:1511.08963, 2015 | 34 | 2015 |
| Learning Linear Causal Representations from Interventions under General Nonlinear Mixing S Buchholz, G Rajendran, E Rosenfeld, B Aragam, B Schölkopf, ... Advances in Neural Information Processing Systems 36, 2023 | 33 | 2023 |
| Globally optimal score-based learning of directed acyclic graphs in high-dimensions B Aragam, A Amini, Q Zhou Advances in Neural Information Processing Systems, 4450-4462, 2019 | 24 | 2019 |
| Fundamental limits and tradeoffs in invariant representation learning H Zhao, C Dan, B Aragam, TS Jaakkola, GJ Gordon, P Ravikumar Journal of Machine Learning Research 23 (340), 1-49, 2022 | 23 | 2022 |
| Variable selection in heterogeneous datasets: a truncated-rank sparse linear mixed model with applications to genome-wide association studies H Wang, B Aragam, EP Xing Methods 145, 2-9, 2018 | 22 | 2018 |
| Personalized Regression Enables Sample-Specific Pan-Cancer Analysis B Lengerich, B Aragam, EP Xing Bioinformatics 34 (13), i178--i186, 2018 | 22 | 2018 |
| Learning Sample-Specific Models with Low-Rank Personalized Regression B Lengerich, B Aragam, EP Xing Advances in Neural Information Processing Systems, 3575-3585, 2019 | 21 | 2019 |
| Learning nonparametric latent causal graphs with unknown interventions Y Jiang, B Aragam Advances in Neural Information Processing Systems 36, 2023 | 19 | 2023 |
Pradeep RavikumarProfessor, School of Computer Science, Carnegie Mellon University在 cs.cmu.edu 的电子邮件经过验证
