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Bryon Aragam
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Cited by
Year
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
8012018
Learning Sparse Nonparametric DAGs
X Zheng, C Dan, B Aragam, P Ravikumar, E Xing
International Conference on Artificial Intelligence and Statistics, 3414-3425, 2020
2432020
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
1462020
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
1362019
Concave penalized estimation of sparse Gaussian Bayesian networks
B Aragam, Q Zhou
Journal of Machine Learning Research 16, 2273-2328, 2015
1232015
Learning Large-Scale Bayesian Networks with the sparsebn Package
B Aragam, J Gu, Q Zhou
Journal of Statistical Software 91 (11), 2019
722019
Identifiability of deep generative models without auxiliary information
B Kivva, G Rajendran, P Ravikumar, B Aragam
Advances in Neural Information Processing Systems 35, 2022
48*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
432022
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
422021
Fault Tolerance in Iterative-Convergent Machine Learning
A Qiao, B Aragam, B Zhang, EP Xing
International Conference on Machine Learning, 5220-5230, 2019
412019
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
372020
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
342020
Learning directed acyclic graphs with penalized neighbourhood regression
B Aragam, AA Amini, Q Zhou
arXiv preprint arXiv:1511.08963, 2015
322015
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
262023
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
222018
Personalized Regression Enables Sample-Specific Pan-Cancer Analysis
B Lengerich, B Aragam, EP Xing
Bioinformatics 34 (13), i178--i186, 2018
222018
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
212019
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
192022
Learning Sample-Specific Models with Low-Rank Personalized Regression
B Lengerich, B Aragam, EP Xing
Advances in Neural Information Processing Systems, 3575-3585, 2019
192019
Structure learning in polynomial time: Greedy algorithms, Bregman information, and exponential families
G Rajendran, B Kivva, M Gao, B Aragam
Advances in Neural Information Processing Systems 34, 18660-18672, 2021
182021
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