Bryon Aragam

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All published works

Action	Title	Year	Authors
+ PDF Chat	Dimension-independent rates for structured neural density estimation	2024	Robert A. Vandermeulen Wai Ming Tai Bryon Aragam
+ PDF Chat	Identifying General Mechanism Shifts in Linear Causal Representations	2024	Tianyu Chen Kevin Bello Francesco Locatello Bryon Aragam Pradeep Ravikumar
+ PDF Chat	Model-free Estimation of Latent Structure via Multiscale Nonparametric Maximum Likelihood	2024	Bryon Aragam Ruiyi Yang
+ PDF Chat	Breaking the curse of dimensionality in structured density estimation	2024	Robert A. Vandermeulen Wai Ming Tai Bryon Aragam
+ PDF Chat	Likelihood-based Differentiable Structure Learning	2024	Chang Deng Kevin Bello Pradeep Ravikumar Bryon Aragam
+ PDF Chat	Do LLMs dream of elephants (when told not to)? Latent concept association and associative memory in transformers	2024	Yibo Jiang Goutham Rajendran Pradeep Ravikumar Bryon Aragam
+ PDF Chat	Greedy equivalence search for nonparametric graphical models	2024	Bryon Aragam
+ PDF Chat	On the Origins of Linear Representations in Large Language Models	2024	Yibo Jiang Goutham Rajendran Pradeep Ravikumar Bryon Aragam Victor Veitch
+ PDF Chat	Learning Interpretable Concepts: Unifying Causal Representation Learning and Foundation Models	2024	Goutham Rajendran Simon Buchholz Bryon Aragam Bernhard Schölkopf Pradeep Ravikumar
+ PDF Chat	Optimal estimation of Gaussian (poly)trees	2024	Yuhao Wang Ming Gao Wai Ming Tai Bryon Aragam Arnab Bhattacharyya
+ PDF Chat	Uniform consistency in nonparametric mixture models	2023	Bryon Aragam Ruiyi Yang
+	Learning Mixtures of Gaussians with Censored Data	2023	Wai Ming Tai Bryon Aragam
+	Optimizing NOTEARS Objectives via Topological Swaps	2023	Chang Deng Kevin Bello Bryon Aragam Pradeep Ravikumar
+	Neuro-Causal Factor Analysis	2023	Alex Markham Ming-Yu Liu Bryon Aragam Liam Solus
+	Learning Linear Causal Representations from Interventions under General Nonlinear Mixing	2023	Simon Buchholz Goutham Rajendran Elan Rosenfeld Bryon Aragam Bernhard Schölkopf Pradeep Ravikumar
+	Optimal neighbourhood selection in structural equation models	2023	Ming Gao Wai Ming Tai Bryon Aragam
+	Learning nonparametric latent causal graphs with unknown interventions	2023	Yibo Jiang Bryon Aragam
+	iSCAN: Identifying Causal Mechanism Shifts among Nonlinear Additive Noise Models	2023	Tianyu Chen Kevin Bello Bryon Aragam Pradeep Ravikumar
+	Global Optimality in Bivariate Gradient-based DAG Learning	2023	Chang Deng Kevin Bello Bryon Aragam Pradeep Ravikumar
+	Assumption violations in causal discovery and the robustness of score matching	2023	Francesco Montagna Atalanti A. Mastakouri Elias Eulig Nicoletta Noceti Lorenzo Rosasco Dominik Janzing Bryon Aragam Francesco Locatello
+	Uncovering Meanings of Embeddings via Partial Orthogonality	2023	Yibo Jiang Bryon Aragam Victor Veitch
+	Inconsistency of cross-validation for structure learning in Gaussian graphical models	2023	Zhao Lyu Wai Ming Tai Mladen Kolar Bryon Aragam
+ PDF Chat	Identifiability of deep generative models under mixture priors without auxiliary information	2022	Bohdan Kivva Goutham Rajendran Pradeep Ravikumar Bryon Aragam
+	Trade-offs of Linear Mixed Models in Genome-Wide Association Studies	2022	Haohan Wang Bryon Aragam Eric P. Xing
+	Optimal estimation of Gaussian DAG models	2022	Ming Gao Wai Ming Tai Bryon Aragam
+	A non-graphical representation of conditional independence via the neighbourhood lattice	2022	Arash Amini Bryon Aragam Qing Zhou
+	Tight Bounds on the Hardness of Learning Simple Nonparametric Mixtures	2022	Bryon Aragam Wai Ming Tai
+	DAGMA: Learning DAGs via M-matrices and a Log-Determinant Acyclicity Characterization	2022	Kevin Bello Bryon Aragam Pradeep Ravikumar
+	Identifiability of deep generative models without auxiliary information	2022	Bohdan Kivva Goutham Rajendran Pradeep Ravikumar Bryon Aragam
+	Structure learning in polynomial time: Greedy algorithms, Bregman information, and exponential families	2021	Goutham Rajendran Bohdan Kivva Ming Gao Bryon Aragam
+	NOTMAD: Estimating Bayesian Networks with Sample-Specific Structures and Parameters	2021	Benjamin J. Lengerich Caleb N. Ellington Bryon Aragam Eric P. Xing Manolis Kellis
+ PDF Chat	Efficient Bayesian network structure learning via local Markov boundary search	2021	Ming Gao Bryon Aragam
+	Structure learning in polynomial time: Greedy algorithms, Bregman information, and exponential families	2021	Goutham Rajendran Bohdan Kivva Ming Gao Bryon Aragam
+	Learning latent causal graphs via mixture oracles	2021	Bohdan Kivva Goutham Rajendran Pradeep Ravikumar Bryon Aragam
+	Fundamental Limits and Tradeoffs in Invariant Representation Learning	2021	Han Zhao Dan Chen Bryon Aragam Tommi Jaakkola Geoff Gordon Pradeep Ravikumar
+	Uniform Consistency in Nonparametric Mixture Models	2021	Bryon Aragam Ruiyi Yang
+	Efficient Bayesian network structure learning via local Markov boundary search	2021	Ming Gao Bryon Aragam
+	Tradeoffs of Linear Mixed Models in Genome-wide Association Studies	2021	Haohan Wang Bryon Aragam Eric P. Xing
+	NOTMAD: Estimating Bayesian Networks with Sample-Specific Structures and Parameters	2021	Ben Lengerich Caleb Ellington Bryon Aragam Eric P. Xing Manolis Kellis
+	Structure learning in polynomial time: Greedy algorithms, Bregman information, and exponential families	2021	Goutham Rajendran Bohdan Kivva Ming Gao Bryon Aragam
+	Learning latent causal graphs via mixture oracles	2021	Bohdan Kivva Goutham Rajendran Pradeep Ravikumar Bryon Aragam
+ PDF Chat	Identifiability of nonparametric mixture models and Bayes optimal clustering	2020	Bryon Aragam Dan Chen Eric P. Xing Pradeep Ravikumar
+ PDF Chat	A polynomial-time algorithm for learning nonparametric causal graphs	2020	Ming Gao Yi Ding Bryon Aragam
+	DYNOTEARS: Structure Learning from Time-Series Data	2020	Roxana Pamfil Nisara Sriwattanaworachai Shaan Desai Philip Pilgerstorfer Paul Beaumont Konstantinos Georgatzis Bryon Aragam
+	A polynomial-time algorithm for learning nonparametric causal graphs	2020	Ming Gao Yi Ding Bryon Aragam
+	DYNOTEARS: Structure Learning from Time-Series Data	2020	Roxana Pamfil Nisara Sriwattanaworachai Shaan Desai Philip Pilgerstorfer Paul R Beaumont Konstantinos Georgatzis Bryon Aragam
+	Fundamental Limits and Tradeoffs in Invariant Representation Learning	2020	Han Zhao Dan Chen Bryon Aragam Tommi Jaakkola Geoffrey J. Gordon Pradeep Ravikumar
+	Diagnostic Curves for Black Box Models.	2019	David I. Inouye Liu Leqi Joon Sik Kim Bryon Aragam Pradeep Ravikumar
+	Automated Dependence Plots	2019	David I. Inouye Liu Leqi Joon Sik Kim Bryon Aragam Pradeep Ravikumar
+	Automated Dependence Plots.	2019	David I. Inouye Liu Leqi Joon Sik Kim Bryon Aragam Pradeep Ravikumar
+	Learning Sample-Specific Models with Low-Rank Personalized Regression	2019	Benjamin J. Lengerich Bryon Aragam Eric P. Xing
+	Learning Sparse Nonparametric DAGs.	2019	Xun Zheng Dan Chen Bryon Aragam Pradeep Ravikumar Eric P. Xing
+	Globally optimal score-based learning of directed acyclic graphs in high-dimensions	2019	Bryon Aragam Arash A. Amini Qing Zhou
+	On perfectness in Gaussian graphical models	2019	Arash A. Amini Bryon Aragam Qing Zhou
+	Learning Sample-Specific Models with Low-Rank Personalized Regression	2019	Benjamin J. Lengerich Bryon Aragam Eric P. Xing
+ PDF Chat	Learning Large-Scale Bayesian Networks with the <b>sparsebn</b> Package	2019	Bryon Aragam Jiaying Gu Qing Zhou
+	Automated Dependence Plots	2019	David I. Inouye Liu Leqi Joon Sik Kim Bryon Aragam Pradeep Ravikumar
+	Learning Sparse Nonparametric DAGs	2019	Xun Zheng Dan Chen Bryon Aragam Pradeep Ravikumar Eric P. Xing
+ PDF Chat	Precision Lasso: accounting for correlations and linear dependencies in high-dimensional genomic data	2018	Haohan Wang Benjamin J. Lengerich Bryon Aragam Eric P. Xing
+	DAGs with NO TEARS: Smooth Optimization for Structure Learning.	2018	Xun Zheng Bryon Aragam Pradeep Ravikumar Eric P. Xing
+	Identifiability of Nonparametric Mixture Models and Bayes Optimal Clustering	2018	Bryon Aragam Dan Chen Pradeep Ravikumar Eric P. Xing
+	Sample Complexity of Nonparametric Semi-Supervised Learning	2018	Dan Chen Liu Leqi Bryon Aragam Pradeep Ravikumar Eric P. Xing
+	Fault Tolerance in Iterative-Convergent Machine Learning	2018	Aurick Qiao Bryon Aragam Bingjing Zhang Eric P. Xing
+	DAGs with NO TEARS: Continuous Optimization for Structure Learning	2018	Xun Zheng Bryon Aragam Pradeep Ravikumar Eric P. Xing
+	Identifiability of Nonparametric Mixture Models and Bayes Optimal Clustering	2018	Bryon Aragam Dan Chen Eric P. Xing Pradeep Ravikumar
+	Partial correlation graphs and the neighborhood lattice.	2017	Arash A. Amini Bryon Aragam Qing Zhou
+	The neighborhood lattice for encoding partial correlations in a Hilbert space	2017	Arash A. Amini Bryon Aragam Qing Zhou
+	Learning Directed Acyclic Graphs with Penalized Neighbourhood Regression	2015	Bryon Aragam Arash A. Amini Qing Zhou
+	Concave Penalized Estimation of Sparse Gaussian Bayesian Networks	2014	Bryon Aragam Qing Zhou
+	Concave Penalized Estimation of Sparse Bayesian Networks.	2014	Bryon Aragam Qing Zhou
+	Concave Penalized Estimation of Sparse Gaussian Bayesian Networks	2014	Bryon Aragam Qing Zhou

Common Coauthors

Coauthor	Papers Together
Pradeep Ravikumar	27
Eric P. Xing	16
Goutham Rajendran	11
Qing Zhou	9
Dan Chen	8
Wai Ming Tai	8
Ming Gao	7
Bohdan Kivva	7
Kevin Bello	6
Arash A. Amini	5
Liu Leqi	5
David I. Inouye	4
Benjamin J. Lengerich	4
Joon Sik Kim	4
Yibo Jiang	4
Haohan Wang	3
Ruiyi Yang	3
Chang Deng	3
Xun Zheng	3
Francesco Locatello	2
Victor Veitch	2
Nisara Sriwattanaworachai	2
Konstantinos Georgatzis	2
Ming Gao	2
Manolis Kellis	2
Philip Pilgerstorfer	2
Bernhard Schölkopf	2
Tianyu Chen	2
Roxana Pamfil	2
Yi Ding	2
Caleb Ellington	2
Tommi Jaakkola	2
Simon Buchholz	2
Robert A. Vandermeulen	2
Shaan Desai	2
Jiaying Gu	1
Bingjing Zhang	1
Geoffrey J. Gordon	1
Dominik Janzing	1
Ben Lengerich	1
Mladen Kolar	1
Arash Amini	1
Zhao Lyu	1
Alex Markham	1
Geoff Gordon	1
Lorenzo Rosasco	1
Pradeep Ravikumar	1
Liam Solus	1
Nicoletta Noceti	1
Aurick Qiao	1

Commonly Cited References

Action	Title	Year	Authors	# of times referenced
+	$\ell_{0}$-penalized maximum likelihood for sparse directed acyclic graphs	2013	Sara van de Geer Peter Bühlmann	8
+	High-dimensional learning of linear causal networks via inverse covariance estimation	2013	Po‐Ling Loh Peter Bühlmann	8
+ PDF Chat	Identifiability of Gaussian structural equation models with equal error variances	2013	Jonas Peters Peter Bühlmann	7
+ PDF Chat	Nearly unbiased variable selection under minimax concave penalty	2010	Cun‐Hui Zhang	7
+	Model Selection Through Sparse Maximum Likelihood Estimation for Multivariate Gaussian or Binary Data	2008	Onureena Banerjee Laurent El Ghaoui Alexandre d’Aspremont	7
+	Learning Directed Acyclic Graphs with Penalized Neighbourhood Regression	2015	Bryon Aragam Arash A. Amini Qing Zhou	7
+	Concave Penalized Estimation of Sparse Gaussian Bayesian Networks	2014	Bryon Aragam Qing Zhou	6
+ PDF Chat	Learning Sparse Causal Gaussian Networks With Experimental Intervention: Regularization and Coordinate Descent	2012	Fei Fu Qing Zhou	6
+	CAM: Causal additive models, high-dimensional order search and penalized regression	2014	Peter Bühlmann Jonas Peters Jan Ernest	6
+	A General Theory of Concave Regularization for High-Dimensional Sparse Estimation Problems	2012	Cun‐Hui Zhang Tong Zhang	6
+ PDF Chat	Penalized likelihood methods for estimation of sparse high-dimensional directed acyclic graphs	2010	Ali Shojaie George Michailidis	6
+	High-dimensional graphs and variable selection with the Lasso	2006	Nicolai Meinshausen Peter Bühlmann	6
+	A* Lasso for Learning a Sparse Bayesian Network Structure for Continuous Variables	2013	Jing Xiang Se Young Kim	6
+	Estimating high-dimensional directed acyclic graphs with the PC-algorithm	2005	Markus Kalisch Peter Bühlmann	5
+	On Model Selection Consistency of Lasso	2006	Peng Zhao Bin Yu	5
+	Sparse inverse covariance estimation with the graphical lasso	2007	Jerome H. Friedman Trevor Hastie R. Tibshirani	5
+	Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties	2001	Jianqing Fan Runze Li	5
+ PDF Chat	Identifiability of Finite Mixtures	1963	Henry Teicher	5
+ PDF Chat	Nonparametric estimation of component distributions in a multivariate mixture	2003	Peter Hall Xiao‐Hua Zhou	5
+ PDF Chat	Optimal Rate of Convergence for Finite Mixture Models	1995	Jiahua Chen	4
+ PDF Chat	Pathwise coordinate optimization	2007	Jerome H. Friedman Trevor Hastie Holger Höfling Robert Tibshirani	4
+	Model-Based Clustering, Discriminant Analysis, and Density Estimation	2002	Chris Fraley Adrian E. Raftery	4
+ PDF Chat	Multi-Domain Sampling With Applications to Structural Inference of Bayesian Networks	2011	Magali Champion Victor Picheny Matthieu Vignes	4
+	Regression Shrinkage and Selection Via the Lasso	1996	Robert Tibshirani	4
+ PDF Chat	Coordinate descent algorithms for lasso penalized regression	2008	Tong Tong Wu Kenneth Lange	4
+	On the Identifiability of the Post-Nonlinear Causal Model	2012	Kun Zhang Aapo Hyvärinen	4
+	Convergence of latent mixing measures in finite and infinite mixture models	2013	XuanLong Nguyen	4
+ PDF Chat	The Adaptive Lasso and Its Oracle Properties	2006	Hui Zou	4
+	Causal discovery with continuous additive noise models	2014	Jonas Peters Joris M. Mooij Dominik Janzing Bernhard Schölkopf	4
+	Regularization Paths for Generalized Linear Models via Coordinate Descent.	2010	Jerome H. Friedman Trevor Hastie Rob Tibshirani	4
+ PDF Chat	Semiparametric mixtures of regressions	2011	David R. Hunter Derek S. Young	4
+	Geometry of the faithfulness assumption in causal inference	2013	Caroline Uhler Garvesh Raskutti Peter Bühlmann Bin Yu	4
+ PDF Chat	On the Mixture of Distributions	1960	Henry Teicher	4
+ PDF Chat	Polyhedral aspects of score equivalence in Bayesian network structure learning	2016	James Cussens David Haws Milan Studený	4
+	High-dimensional Ising model selection using ℓ1-regularized logistic regression	2010	Pradeep Ravikumar Martin J. Wainwright John Lafferty	4
+ PDF Chat	Rates of convergence for the Gaussian mixture sieve	2000	Christopher R. Genovese Larry Wasserman	4
+	Learning Identifiable Gaussian Bayesian Networks in Polynomial Time and Sample Complexity	2017	Asish Ghoshal Jean Honorio	4
+	Nonparametric inference in multivariate mixtures	2005	Peter Hall Amnon Neeman Reza Pakyari Ryan Elmore	4
+ PDF Chat	<i>SparseNet</i>: Coordinate Descent With Nonconvex Penalties	2011	Rahul Mazumder Jerome H. Friedman Trevor Hastie	4
+ PDF Chat	On causal discovery with an equal-variance assumption	2019	Wenyu Chen Mathias Drton Y. Samuel Wang	4
+ PDF Chat	Penalized estimation of directed acyclic graphs from discrete data	2018	Jiaying Gu Fei Fu Qing Zhou	4
+ PDF Chat	None	1997	David Maxwell Chickering David Heckerman	3
+	A Selective Overview of Variable Selection in High Dimensional Feature Space.	2010	Jianqing Fan Jinchi Lv	3
+	Covariance matrix selection and estimation via penalised normal likelihood	2006	Jianhua Z. Huang Naiping Liu Mohsen Pourahmadi Linxu Liu	3
+ PDF Chat	Data spectroscopy: Eigenspaces of convolution operators and clustering	2009	Tao Shi Mikhail A. Belkin Bin Yu	3
+	Finding optimal bayesian networks	2002	David Maxwell Chickering Christopher Meek	3
+ PDF Chat	Learning mixtures of separated nonspherical Gaussians	2005	Sanjeev Arora Ravi Kannan	3
+	The relative value of labeled and unlabeled samples in pattern recognition with an unknown mixing parameter	1996	V. Castelli Thomas M. Cover	3
+	Learning Bayesian Networks with the<b>bnlearn</b><i>R</i>Package	2010	Marco Scutari	3
+ PDF Chat	Tuning parameter selectors for the smoothly clipped absolute deviation method	2007	Hansheng Wang Runze Li C.-L. Tsai	3