Bryon Aragam

Publication Activity (10 Years)

Years Active: 2014-2024
Publications (10 Years): 70

Top Topics

Sample Complexity

Conditional Independence

Gaussian Graphical Models

Top Venues

Publications

Yuhao Wang, Ming Gao, Wai Ming Tai, Bryon Aragam, Arnab Bhattacharyya
Optimal estimation of Gaussian (poly)trees. CoRR (2024)
Bryon Aragam
Greedy equivalence search for nonparametric graphical models. CoRR (2024)
Yibo Jiang, Goutham Rajendran, Pradeep Ravikumar, Bryon Aragam, Victor Veitch
On the Origins of Linear Representations in Large Language Models. CoRR (2024)
Yuhao Wang, Ming Gao, Wai Ming Tai, Bryon Aragam, Arnab Bhattacharyya
Optimal estimation of Gaussian (poly)trees. AISTATS (2024)
Yibo Jiang, Goutham Rajendran, Pradeep Ravikumar, Bryon Aragam
Do LLMs dream of elephants (when told not to)? Latent concept association and associative memory in transformers. CoRR (2024)
Goutham Rajendran, Simon Buchholz, Bryon Aragam, Bernhard Schölkopf, Pradeep Ravikumar
Learning Interpretable Concepts: Unifying Causal Representation Learning and Foundation Models. CoRR (2024)
Zhao Lyu, Wai Ming Tai, Mladen Kolar, Bryon Aragam
Inconsistency of Cross-Validation for Structure Learning in Gaussian Graphical Models. AISTATS (2024)
Yibo Jiang, Bryon Aragam, Victor Veitch
Uncovering Meanings of Embeddings via Partial Orthogonality. NeurIPS (2023)
Wai Ming Tai, Bryon Aragam
Learning Mixtures of Gaussians with Censored Data. CoRR (2023)
Francesco Montagna, Atalanti-Anastasia Mastakouri, Elias Eulig, Nicoletta Noceti, Lorenzo Rosasco, Dominik Janzing, Bryon Aragam, Francesco Locatello
Assumption violations in causal discovery and the robustness of score matching. CoRR (2023)
Wai Ming Tai, Bryon Aragam
Tight Bounds on the Hardness of Learning Simple Nonparametric Mixtures. COLT (2023)
Wai Ming Tai, Bryon Aragam
Learning Mixtures of Gaussians with Censored Data. ICML (2023)
Yibo Jiang, Bryon Aragam, Victor Veitch
Uncovering Meanings of Embeddings via Partial Orthogonality. CoRR (2023)
Alex Markham, Mingyu Liu, Bryon Aragam, Liam Solus
Neuro-Causal Factor Analysis. CoRR (2023)
Chang Deng, Kevin Bello, Bryon Aragam, Pradeep Ravikumar
Global Optimality in Bivariate Gradient-based DAG Learning. CoRR (2023)
Simon Buchholz, Goutham Rajendran, Elan Rosenfeld, Bryon Aragam, Bernhard Schölkopf, Pradeep Ravikumar
Learning Linear Causal Representations from Interventions under General Nonlinear Mixing. CoRR (2023)
Yibo Jiang, Bryon Aragam
Learning nonparametric latent causal graphs with unknown interventions. CoRR (2023)
Simon Buchholz, Goutham Rajendran, Elan Rosenfeld, Bryon Aragam, Bernhard Schölkopf, Pradeep Ravikumar
Learning Linear Causal Representations from Interventions under General Nonlinear Mixing. NeurIPS (2023)
Chang Deng, Kevin Bello, Bryon Aragam, Pradeep Ravikumar
Optimizing NOTEARS Objectives via Topological Swaps. CoRR (2023)
Francesco Montagna, Atalanti-Anastasia Mastakouri, Elias Eulig, Nicoletta Noceti, Lorenzo Rosasco, Dominik Janzing, Bryon Aragam, Francesco Locatello
Assumption violations in causal discovery and the robustness of score matching. NeurIPS (2023)
Yibo Jiang, Bryon Aragam
Learning Nonparametric Latent Causal Graphs with Unknown Interventions. NeurIPS (2023)
Tianyu Chen, Kevin Bello, Bryon Aragam, Pradeep Ravikumar
iSCAN: Identifying Causal Mechanism Shifts among Nonlinear Additive Noise Models. CoRR (2023)
Zhao Lyu, Wai Ming Tai, Mladen Kolar, Bryon Aragam
Inconsistency of cross-validation for structure learning in Gaussian graphical models. CoRR (2023)
Chang Deng, Kevin Bello, Bryon Aragam, Pradeep Kumar Ravikumar
Optimizing NOTEARS Objectives via Topological Swaps. ICML (2023)
Tianyu Chen, Kevin Bello, Bryon Aragam, Pradeep Ravikumar
iSCAN: Identifying Causal Mechanism Shifts among Nonlinear Additive Noise Models. NeurIPS (2023)
Chang Deng, Kevin Bello, Pradeep Ravikumar, Bryon Aragam
Global Optimality in Bivariate Gradient-based DAG Learning. NeurIPS (2023)
Arash A. Amini, Bryon Aragam, Qing Zhou
On perfectness in Gaussian graphical models. AISTATS (2022)
Ming Gao, Wai Ming Tai, Bryon Aragam
Optimal estimation of Gaussian DAG models. CoRR (2022)
Arash A. Amini, Bryon Aragam, Qing Zhou
A non-graphical representation of conditional independence via the neighbourhood lattice. CoRR (2022)
Ming Gao, Wai Ming Tai, Bryon Aragam
Optimal estimation of Gaussian DAG models. AISTATS (2022)
Han Zhao, Chen Dan, Bryon Aragam, Tommi S. Jaakkola, Geoffrey J. Gordon, Pradeep Ravikumar
Fundamental Limits and Tradeoffs in Invariant Representation Learning. J. Mach. Learn. Res. 23 (2022)
Haohan Wang, Bryon Aragam, Eric P. Xing
Trade-offs of Linear Mixed Models in Genome-Wide Association Studies. J. Comput. Biol. 29 (3) (2022)
Bryon Aragam, Wai Ming Tai
A super-polynomial lower bound for learning nonparametric mixtures. CoRR (2022)
Kevin Bello, Bryon Aragam, Pradeep Ravikumar
DAGMA: Learning DAGs via M-matrices and a Log-Determinant Acyclicity Characterization. NeurIPS (2022)
Bohdan Kivva, Goutham Rajendran, Pradeep Ravikumar, Bryon Aragam
Identifiability of deep generative models without auxiliary information. NeurIPS (2022)
Bohdan Kivva, Goutham Rajendran, Pradeep Ravikumar, Bryon Aragam
Identifiability of deep generative models under mixture priors without auxiliary information. CoRR (2022)
Kevin Bello, Bryon Aragam, Pradeep Ravikumar
DAGMA: Learning DAGs via M-matrices and a Log-Determinant Acyclicity Characterization. CoRR (2022)
Bohdan Kivva, Goutham Rajendran, Pradeep Ravikumar, Bryon Aragam
Learning latent causal graphs via mixture oracles. CoRR (2021)
Goutham Rajendran, Bohdan Kivva, Ming Gao, Bryon Aragam
Structure learning in polynomial time: Greedy algorithms, Bregman information, and exponential families. CoRR (2021)
Goutham Rajendran, Bohdan Kivva, Ming Gao, Bryon Aragam
Structure learning in polynomial time: Greedy algorithms, Bregman information, and exponential families. NeurIPS (2021)
Haohan Wang, Bryon Aragam, Eric P. Xing
Tradeoffs of Linear Mixed Models in Genome-wide Association Studies. CoRR (2021)
Ming Gao, Bryon Aragam
Efficient Bayesian network structure learning via local Markov boundary search. CoRR (2021)
Ming Gao, Bryon Aragam
Efficient Bayesian network structure learning via local Markov boundary search. NeurIPS (2021)
Bohdan Kivva, Goutham Rajendran, Pradeep Ravikumar, Bryon Aragam
Learning latent causal graphs via mixture oracles. NeurIPS (2021)
Benjamin Lengerich, Caleb Ellington, Bryon Aragam, Eric P. Xing, Manolis Kellis
NOTMAD: Estimating Bayesian Networks with Sample-Specific Structures and Parameters. CoRR (2021)
Roxana Pamfil, Nisara Sriwattanaworachai, Shaan Desai, Philip Pilgerstorfer, Paul Beaumont, Konstantinos Georgatzis, Bryon Aragam
DYNOTEARS: Structure Learning from Time-Series Data. CoRR (2020)
Ming Gao, Yi Ding, Bryon Aragam
A polynomial-time algorithm for learning nonparametric causal graphs. CoRR (2020)
Han Zhao, Chen Dan, Bryon Aragam, Tommi S. Jaakkola, Geoffrey J. Gordon, Pradeep Ravikumar
Fundamental Limits and Tradeoffs in Invariant Representation Learning. CoRR (2020)
Xun Zheng, Chen Dan, Bryon Aragam, Pradeep Ravikumar, Eric P. Xing
Learning Sparse Nonparametric DAGs. AISTATS (2020)
Ming Gao, Yi Ding, Bryon Aragam
A polynomial-time algorithm for learning nonparametric causal graphs. NeurIPS (2020)
Roxana Pamfil, Nisara Sriwattanaworachai, Shaan Desai, Philip Pilgerstorfer, Konstantinos Georgatzis, Paul Beaumont, Bryon Aragam
DYNOTEARS: Structure Learning from Time-Series Data. AISTATS (2020)
David I. Inouye, Liu Leqi, Joon Sik Kim, Bryon Aragam, Pradeep Ravikumar
Automated Dependence Plots. UAI (2020)
Xun Zheng, Chen Dan, Bryon Aragam, Pradeep Ravikumar, Eric P. Xing
Learning Sparse Nonparametric DAGs. CoRR (2019)
Haohan Wang, Benjamin J. Lengerich, Bryon Aragam, Eric P. Xing
Precision Lasso: accounting for correlations and linear dependencies in high-dimensional genomic data. Bioinform. 35 (7) (2019)
Benjamin J. Lengerich, Bryon Aragam, Eric P. Xing
Learning Sample-Specific Models with Low-Rank Personalized Regression. NeurIPS (2019)
Bryon Aragam, Arash A. Amini, Qing Zhou
Globally optimal score-based learning of directed acyclic graphs in high-dimensions. NeurIPS (2019)
Arash A. Amini, Bryon Aragam, Qing Zhou
On perfectness in Gaussian graphical models. CoRR (2019)
Aurick Qiao, Bryon Aragam, Bingjing Zhang, Eric P. Xing
Fault Tolerance in Iterative-Convergent Machine Learning. ICML (2019)
David I. Inouye, Liu Leqi, Joon Sik Kim, Bryon Aragam, Pradeep Ravikumar
Diagnostic Curves for Black Box Models. CoRR (2019)
Benjamin J. Lengerich, Bryon Aragam, Eric P. Xing
Learning Sample-Specific Models with Low-Rank Personalized Regression. CoRR (2019)
Chen Dan, Liu Leqi, Bryon Aragam, Pradeep Ravikumar, Eric P. Xing
The Sample Complexity of Semi-Supervised Learning with Nonparametric Mixture Models. NeurIPS (2018)
Benjamin J. Lengerich, Bryon Aragam, Eric P. Xing
Personalized regression enables sample-specific pan-cancer analysis. Bioinform. 34 (13) (2018)
Bryon Aragam, Chen Dan, Pradeep Ravikumar, Eric P. Xing
Identifiability of Nonparametric Mixture Models and Bayes Optimal Clustering. CoRR (2018)
Chen Dan, Liu Leqi, Bryon Aragam, Pradeep Ravikumar, Eric P. Xing
Sample Complexity of Nonparametric Semi-Supervised Learning. CoRR (2018)
Aurick Qiao, Bryon Aragam, Bingjing Zhang, Eric P. Xing
Fault Tolerance in Iterative-Convergent Machine Learning. CoRR (2018)
Xun Zheng, Bryon Aragam, Pradeep Ravikumar, Eric P. Xing
DAGs with NO TEARS: Smooth Optimization for Structure Learning. CoRR (2018)
Xun Zheng, Bryon Aragam, Pradeep Ravikumar, Eric P. Xing
DAGs with NO TEARS: Continuous Optimization for Structure Learning. NeurIPS (2018)
Bryon Aragam, Jiaying Gu, Qing Zhou
Learning Large-Scale Bayesian Networks with the sparsebn Package. CoRR (2017)
Arash A. Amini, Bryon Aragam, Qing Zhou
Partial correlation graphs and the neighborhood lattice. CoRR (2017)
Haohan Wang, Bryon Aragam, Eric P. Xing
Variable selection in heterogeneous datasets: A truncated-rank sparse linear mixed model with applications to genome-wide association studies. BIBM (2017)
Bryon Aragam, Arash A. Amini, Qing Zhou
Learning Directed Acyclic Graphs with Penalized Neighbourhood Regression. CoRR (2015)
Bryon Aragam, Qing Zhou
Concave penalized estimation of sparse Gaussian Bayesian networks. J. Mach. Learn. Res. 16 (2015)
Bryon Aragam, Qing Zhou
Concave Penalized Estimation of Sparse Bayesian Networks. CoRR (2014)