Bryon Aragam

Atalanti-Anastasia Mastakouri

Pradeep Ravikumar

Proceedings of the Compendium of Neurosymbolic Artificial Intelligence, 2023

Inconsistency of cross-validation for structure learning in Gaussian graphical models.

[BibT_eX]

[DOI]

CoRR, 2023

Neuro-Causal Factor Analysis.

[BibT_eX]

[DOI]

CoRR, 2023

Assumption violations in causal discovery and the robustness of score matching.

[BibT_eX]

[DOI]

Francesco Montagna

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Uncovering Meanings of Embeddings via Partial Orthogonality.

[BibT_eX]

[DOI]

Yibo Jiang

Victor Veitch

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Learning Nonparametric Latent Causal Graphs with Unknown Interventions.

[BibT_eX]

[DOI]

Yibo Jiang

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Global Optimality in Bivariate Gradient-based DAG Learning.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

iSCAN: Identifying Causal Mechanism Shifts among Nonlinear Additive Noise Models.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Learning Linear Causal Representations from Interventions under General Nonlinear Mixing.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Learning Mixtures of Gaussians with Censored Data.

[BibT_eX]

[DOI]

Proceedings of the International Conference on Machine Learning, 2023

Optimizing NOTEARS Objectives via Topological Swaps.

[BibT_eX]

[DOI]

Chang Deng

Kevin Bello

Pradeep Kumar Ravikumar

Proceedings of the International Conference on Machine Learning, 2023

Tight Bounds on the Hardness of Learning Simple Nonparametric Mixtures.

[BibT_eX]

[DOI]

Proceedings of the Thirty Sixth Annual Conference on Learning Theory, 2023

2022

Fundamental Limits and Tradeoffs in Invariant Representation Learning.

[BibT_eX]

[DOI]

J. Mach. Learn. Res., 2022

Trade-offs of Linear Mixed Models in Genome-Wide Association Studies.

[BibT_eX]

[DOI]

J. Comput. Biol., 2022

Identifiability of deep generative models under mixture priors without auxiliary information.

[BibT_eX]

[DOI]

CoRR, 2022

A non-graphical representation of conditional independence via the neighbourhood lattice.

[BibT_eX]

[DOI]

CoRR, 2022

A super-polynomial lower bound for learning nonparametric mixtures.

[BibT_eX]

[DOI]

CoRR, 2022

Identifiability of deep generative models without auxiliary information.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

DAGMA: Learning DAGs via M-matrices and a Log-Determinant Acyclicity Characterization.

[BibT_eX]

[DOI]

Kevin Bello

Pradeep Ravikumar

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Optimal estimation of Gaussian DAG models.

[BibT_eX]

[DOI]

Ming Gao

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

On perfectness in Gaussian graphical models.

[BibT_eX]

[DOI]

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

2021

Tradeoffs of Linear Mixed Models in Genome-wide Association Studies.

[BibT_eX]

[DOI]

CoRR, 2021

NOTMAD: Estimating Bayesian Networks with Sample-Specific Structures and Parameters.

[BibT_eX]

[DOI]

CoRR, 2021

Structure learning in polynomial time: Greedy algorithms, Bregman information, and exponential families.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Learning latent causal graphs via mixture oracles.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Efficient Bayesian network structure learning via local Markov boundary search.

[BibT_eX]

[DOI]

Ming Gao

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

2020

Automated Dependence Plots.

[BibT_eX]

[DOI]

Proceedings of the Thirty-Sixth Conference on Uncertainty in Artificial Intelligence, 2020

A polynomial-time algorithm for learning nonparametric causal graphs.

[BibT_eX]

[DOI]

Ming Gao

Yi Ding

Nisara Sriwattanaworachai

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Learning Sparse Nonparametric DAGs.

[BibT_eX]

[DOI]

Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

DYNOTEARS: Structure Learning from Time-Series Data.

[BibT_eX]

[DOI]

Roxana Pamfil

Shaan Desai

Philip Pilgerstorfer

Konstantinos Georgatzis

Paul Beaumont

Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

2019

Diagnostic Curves for Black Box Models.

[BibT_eX]

[DOI]

CoRR, 2019

Precision Lasso: accounting for correlations and linear dependencies in high-dimensional genomic data.

[BibT_eX]

[DOI]

Bioinform., 2019

Learning Sample-Specific Models with Low-Rank Personalized Regression.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Globally optimal score-based learning of directed acyclic graphs in high-dimensions.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Fault Tolerance in Iterative-Convergent Machine Learning.

[BibT_eX]

[DOI]

Proceedings of the 36th International Conference on Machine Learning, 2019

2018

Sample Complexity of Nonparametric Semi-Supervised Learning.

[BibT_eX]

[DOI]

CoRR, 2018

DAGs with NO TEARS: Smooth Optimization for Structure Learning.

[BibT_eX]

[DOI]

CoRR, 2018

Identifiability of Nonparametric Mixture Models and Bayes Optimal Clustering.

[BibT_eX]

[DOI]

CoRR, 2018

Personalized regression enables sample-specific pan-cancer analysis.

[BibT_eX]

[DOI]

Bioinform., 2018

DAGs with NO TEARS: Continuous Optimization for Structure Learning.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

The Sample Complexity of Semi-Supervised Learning with Nonparametric Mixture Models.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

2017

Partial correlation graphs and the neighborhood lattice.

[BibT_eX]

[DOI]

CoRR, 2017

Learning Large-Scale Bayesian Networks with the sparsebn Package.

[BibT_eX]

[DOI]

Jiaying Gu

CoRR, 2017

Variable selection in heterogeneous datasets: A truncated-rank sparse linear mixed model with applications to genome-wide association studies.

[BibT_eX]

[DOI]

Proceedings of the 2017 IEEE International Conference on Bioinformatics and Biomedicine, 2017

2015

Concave penalized estimation of sparse Gaussian Bayesian networks.

[BibT_eX]

[DOI]

J. Mach. Learn. Res., 2015

Learning Directed Acyclic Graphs with Penalized Neighbourhood Regression.

[BibT_eX]

[DOI]

CoRR, 2015

2014

Concave Penalized Estimation of Sparse Bayesian Networks.

[BibT_eX]

[DOI]