Concave Penalized Estimation of Sparse Bayesian Networks. - Bryon Aragam; Qing Zhou (CoRR, 2014)

Learning Directed Acyclic Graphs with Penalized Neighbourhood Regression. - Bryon Aragam; Arash A. Amini; Qing Zhou (CoRR, 2015)

Concave penalized estimation of sparse Gaussian Bayesian networks. - Bryon Aragam; Qing Zhou (J. Mach. Learn. Res., 2015)

Variable selection in heterogeneous datasets: A truncated-rank sparse linear mixed model with applications to genome-wide association studies. - Haohan Wang; Bryon Aragam; Eric P. Xing (BIBM, 2017)

Learning Large-Scale Bayesian Networks with the sparsebn Package. - Bryon Aragam; Jiaying Gu; Qing Zhou (CoRR, 2017)

Partial correlation graphs and the neighborhood lattice. - Arash A. Amini; Bryon Aragam; Qing Zhou (CoRR, 2017)

The Sample Complexity of Semi-Supervised Learning with Nonparametric Mixture Models. - Chen Dan 001; Liu Leqi; Bryon Aragam; Pradeep Ravikumar; Eric P. Xing (NeurIPS, 2018)

DAGs with NO TEARS: Continuous Optimization for Structure Learning. - Xun Zheng; Bryon Aragam; Pradeep Ravikumar; Eric P. Xing (NeurIPS, 2018)

Personalized regression enables sample-specific pan-cancer analysis. - Benjamin J. Lengerich; Bryon Aragam; Eric P. Xing (Bioinform., 2018)

Identifiability of Nonparametric Mixture Models and Bayes Optimal Clustering. - Bryon Aragam; Chen Dan 001; Pradeep Ravikumar; Eric P. Xing (CoRR, 2018)

DAGs with NO TEARS: Smooth Optimization for Structure Learning. - Xun Zheng; Bryon Aragam; Pradeep Ravikumar; Eric P. Xing (CoRR, 2018)

Sample Complexity of Nonparametric Semi-Supervised Learning. - Chen Dan 001; Liu Leqi; Bryon Aragam; Pradeep Ravikumar; Eric P. Xing (CoRR, 2018)

Fault Tolerance in Iterative-Convergent Machine Learning. - Aurick Qiao; Bryon Aragam; Bingjing Zhang; Eric P. Xing (ICML, 2019)

Globally optimal score-based learning of directed acyclic graphs in high-dimensions. - Bryon Aragam; Arash A. Amini; Qing Zhou (NeurIPS, 2019)

Learning Sample-Specific Models with Low-Rank Personalized Regression. - Benjamin J. Lengerich; Bryon Aragam; Eric P. Xing (NeurIPS, 2019)

Precision Lasso: accounting for correlations and linear dependencies in high-dimensional genomic data. - Haohan Wang; Benjamin J. Lengerich; Bryon Aragam; Eric P. Xing (Bioinform., 2019)

Diagnostic Curves for Black Box Models. - David I. Inouye; Liu Leqi; Joon Sik Kim; Bryon Aragam; Pradeep Ravikumar (CoRR, 2019)

DYNOTEARS: Structure Learning from Time-Series Data. - Roxana Pamfil; Nisara Sriwattanaworachai; Shaan Desai; Philip Pilgerstorfer; Konstantinos Georgatzis; Paul Beaumont; Bryon Aragam (AISTATS, 2020)

Learning Sparse Nonparametric DAGs. - Xun Zheng; Chen Dan 001; Bryon Aragam; Pradeep Ravikumar; Eric P. Xing (AISTATS, 2020)

A polynomial-time algorithm for learning nonparametric causal graphs. - Ming Gao; Yi Ding 006; Bryon Aragam (NeurIPS, 2020)

Automated Dependence Plots. - David I. Inouye; Liu Leqi; Joon Sik Kim; Bryon Aragam; Pradeep Ravikumar (UAI, 2020)

Efficient Bayesian network structure learning via local Markov boundary search. - Ming Gao; Bryon Aragam (NeurIPS, 2021)

Learning latent causal graphs via mixture oracles. - Bohdan Kivva; Goutham Rajendran; Pradeep Ravikumar; Bryon Aragam (NeurIPS, 2021)

Structure learning in polynomial time: Greedy algorithms, Bregman information, and exponential families. - Goutham Rajendran; Bohdan Kivva; Ming Gao; Bryon Aragam (NeurIPS, 2021)

NOTMAD: Estimating Bayesian Networks with Sample-Specific Structures and Parameters. - Benjamin J. Lengerich; Caleb Ellington; Bryon Aragam; Eric P. Xing; Manolis Kellis (CoRR, 2021)

Tradeoffs of Linear Mixed Models in Genome-wide Association Studies. - Haohan Wang; Bryon Aragam; Eric P. Xing (CoRR, 2021)

On perfectness in Gaussian graphical models. - Arash A. Amini; Bryon Aragam; Qing Zhou (AISTATS, 2022)

Optimal estimation of Gaussian DAG models. - Ming Gao; Wai Ming Tai; Bryon Aragam (AISTATS, 2022)

DAGMA: Learning DAGs via M-matrices and a Log-Determinant Acyclicity Characterization. - Kevin Bello; Bryon Aragam; Pradeep Ravikumar (NeurIPS, 2022)

Identifiability of deep generative models without auxiliary information. - Bohdan Kivva; Goutham Rajendran; Pradeep Ravikumar; Bryon Aragam (NeurIPS, 2022)

A super-polynomial lower bound for learning nonparametric mixtures. - Bryon Aragam; Wai Ming Tai (CoRR, 2022)

A non-graphical representation of conditional independence via the neighbourhood lattice. - Arash A. Amini; Bryon Aragam; Qing Zhou (CoRR, 2022)

Identifiability of deep generative models under mixture priors without auxiliary information. - Bohdan Kivva; Goutham Rajendran; Pradeep Ravikumar; Bryon Aragam (CoRR, 2022)

Trade-offs of Linear Mixed Models in Genome-Wide Association Studies. - Haohan Wang; Bryon Aragam; Eric P. Xing (J. Comput. Biol., 2022)

Fundamental Limits and Tradeoffs in Invariant Representation Learning. - Han Zhao 002; Chen Dan 001; Bryon Aragam; Tommi S. Jaakkola; Geoffrey J. Gordon; Pradeep Ravikumar (J. Mach. Learn. Res., 2022)

Tight Bounds on the Hardness of Learning Simple Nonparametric Mixtures. - Wai Ming Tai; Bryon Aragam (COLT, 2023)

Optimizing NOTEARS Objectives via Topological Swaps. - Chang Deng; Kevin Bello; Bryon Aragam; Pradeep Kumar Ravikumar (ICML, 2023)

Learning Mixtures of Gaussians with Censored Data. - Wai Ming Tai; Bryon Aragam (ICML, 2023)

Learning Linear Causal Representations from Interventions under General Nonlinear Mixing. - Simon Buchholz; Goutham Rajendran; Elan Rosenfeld; Bryon Aragam; Bernhard Schölkopf; Pradeep Ravikumar (NeurIPS, 2023)

iSCAN: Identifying Causal Mechanism Shifts among Nonlinear Additive Noise Models. - Tianyu Chen; Kevin Bello; Bryon Aragam; Pradeep Ravikumar (NeurIPS, 2023)

Global Optimality in Bivariate Gradient-based DAG Learning. - Chang Deng; Kevin Bello; Pradeep Ravikumar; Bryon Aragam (NeurIPS, 2023)

Learning Nonparametric Latent Causal Graphs with Unknown Interventions. - Yibo Jiang; Bryon Aragam (NeurIPS, 2023)

Uncovering Meanings of Embeddings via Partial Orthogonality. - Yibo Jiang; Bryon Aragam; Victor Veitch (NeurIPS, 2023)

Assumption violations in causal discovery and the robustness of score matching. - Francesco Montagna; Atalanti-Anastasia Mastakouri; Elias Eulig; Nicoletta Noceti; Lorenzo Rosasco; Dominik Janzing; Bryon Aragam; Francesco Locatello (NeurIPS, 2023)

Neuro-Causal Factor Analysis. - Alex Markham; Mingyu Liu; Bryon Aragam; Liam Solus (CoRR, 2023)

Neuro-Causal Models. - Bryon Aragam; Pradeep Ravikumar (Compendium of Neurosymbolic Artificial Intelligence, 2023)

Inconsistency of Cross-Validation for Structure Learning in Gaussian Graphical Models. - Zhao Lyu; Wai Ming Tai; Mladen Kolar; Bryon Aragam (AISTATS, 2024)

Optimal estimation of Gaussian (poly)trees. - Yuhao Wang; Ming Gao; Wai Ming Tai; Bryon Aragam; Arnab Bhattacharyya 001 (AISTATS, 2024)

On the Origins of Linear Representations in Large Language Models. - Yibo Jiang; Goutham Rajendran; Pradeep Kumar Ravikumar; Bryon Aragam; Victor Veitch (ICML, 2024)

Identifying General Mechanism Shifts in Linear Causal Representations. - Tianyu Chen; Kevin Bello; Francesco Locatello; Bryon Aragam; Pradeep Ravikumar (NeurIPS, 2024)

Markov Equivalence and Consistency in Differentiable Structure Learning. - Chang Deng; Kevin Bello; Pradeep Ravikumar; Bryon Aragam (NeurIPS, 2024)

Do LLMs dream of elephants (when told not to)? Latent concept association and associative memory in transformers. - Yibo Jiang; Goutham Rajendran; Pradeep Ravikumar; Bryon Aragam (NeurIPS, 2024)

From Causal to Concept-Based Representation Learning. - Goutham Rajendran; Simon Buchholz; Bryon Aragam; Bernhard Schölkopf; Pradeep Ravikumar (NeurIPS, 2024)

Breaking the curse of dimensionality in structured density estimation. - Robert A. Vandermeulen; Wai Ming Tai; Bryon Aragam (NeurIPS, 2024)

Learning Interpretable Concepts: Unifying Causal Representation Learning and Foundation Models. - Goutham Rajendran; Simon Buchholz; Bryon Aragam; Bernhard Schölkopf; Pradeep Ravikumar (CoRR, 2024)

Greedy equivalence search for nonparametric graphical models. - Bryon Aragam (CoRR, 2024)

Likelihood-based Differentiable Structure Learning. - Chang Deng; Kevin Bello; Pradeep Ravikumar; Bryon Aragam (CoRR, 2024)

Dimension-independent rates for structured neural density estimation. - Robert A. Vandermeulen; Wai Ming Tai; Bryon Aragam (CoRR, 2024)

ScoreFlow: Mastering LLM Agent Workflows via Score-based Preference Optimization. - Yinjie Wang; Ling Yang; Guohao Li 013; Mengdi Wang; Bryon Aragam (CoRR, 2025)

Towards Interpretable Deep Generative Models via Causal Representation Learning. - Gemma E. Moran; Bryon Aragam (CoRR, 2025)

Intervening to learn and compose disentangled representations. - Alex Markham; Jeri A. Chang; Isaac Hirsch; Liam Solus; Bryon Aragam (CoRR, 2025)