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06/30/2023
Global Optimality in Bivariate Gradient-based DAG Learning
Recently, a new class of non-convex optimization problems motivated by t...
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06/30/2023
iSCAN: Identifying Causal Mechanism Shifts among Nonlinear Additive Noise Models
Structural causal models (SCMs) are widely used in various disciplines t...
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06/05/2023
Learning nonparametric latent causal graphs with unknown interventions
We establish conditions under which latent causal graphs are nonparametr...
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06/04/2023
Optimal neighbourhood selection in structural equation models
We study the optimal sample complexity of neighbourhood selection in lin...
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06/04/2023
Learning Linear Causal Representations from Interventions under General Nonlinear Mixing
We study the problem of learning causal representations from unknown, la...
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05/31/2023
Neuro-Causal Factor Analysis
Factor analysis (FA) is a statistical tool for studying how observed var...
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05/26/2023
Optimizing NOTEARS Objectives via Topological Swaps
Recently, an intriguing class of non-convex optimization problems has em...
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05/06/2023
Learning Mixtures of Gaussians with Censored Data
We study the problem of learning mixtures of Gaussians with censored dat...
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09/16/2022
DAGMA: Learning DAGs via M-matrices and a Log-Determinant Acyclicity Characterization
The combinatorial problem of learning directed acyclic graphs (DAGs) fro...
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06/20/2022
Identifiability of deep generative models under mixture priors without auxiliary information
We prove identifiability of a broad class of deep latent variable models...
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06/12/2022
A non-graphical representation of conditional independence via the neighbourhood lattice
We introduce and study the neighbourhood lattice decomposition of a dist...
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03/28/2022
A super-polynomial lower bound for learning nonparametric mixtures
We study the problem of learning nonparametric distributions in a finite...
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01/25/2022
Optimal estimation of Gaussian DAG models
We study the optimal sample complexity of learning a Gaussian directed a...
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11/05/2021
Tradeoffs of Linear Mixed Models in Genome-wide Association Studies
Motivated by empirical arguments that are well-known from the genome-wid...
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11/01/2021
NOTMAD: Estimating Bayesian Networks with Sample-Specific Structures and Parameters
Context-specific Bayesian networks (i.e. directed acyclic graphs, DAGs) ...
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10/12/2021
Efficient Bayesian network structure learning via local Markov boundary search
We analyze the complexity of learning directed acyclic graphical models ...
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10/10/2021
Structure learning in polynomial time: Greedy algorithms, Bregman information, and exponential families
Greedy algorithms have long been a workhorse for learning graphical mode...
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08/31/2021
Uniform Consistency in Nonparametric Mixture Models
We study uniform consistency in nonparametric mixture models as well as ...
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06/29/2021
Learning latent causal graphs via mixture oracles
We study the problem of reconstructing a causal graphical model from dat...
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12/19/2020
Fundamental Limits and Tradeoffs in Invariant Representation Learning
Many machine learning applications involve learning representations that...
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06/22/2020
A polynomial-time algorithm for learning nonparametric causal graphs
We establish finite-sample guarantees for a polynomial-time algorithm fo...
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02/02/2020
DYNOTEARS: Structure Learning from Time-Series Data
In this paper, we revisit the structure learning problem for dynamic Bay...
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12/02/2019
Diagnostic Curves for Black Box Models
In safety-critical applications of machine learning, it is often necessa...
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10/15/2019
Learning Sample-Specific Models with Low-Rank Personalized Regression
Modern applications of machine learning (ML) deal with increasingly hete...
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09/29/2019
Learning Sparse Nonparametric DAGs
We develop a framework for learning sparse nonparametric directed acycli...
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09/03/2019
On perfectness in Gaussian graphical models
Knowing when a graphical model is perfect to a distribution is essential...
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10/17/2018
Fault Tolerance in Iterative-Convergent Machine Learning
Machine learning (ML) training algorithms often possess an inherent self...
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09/10/2018
Sample Complexity of Nonparametric Semi-Supervised Learning
We study the sample complexity of semi-supervised learning (SSL) and int...
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03/04/2018
DAGs with NO TEARS: Smooth Optimization for Structure Learning
Estimating the structure of directed acyclic graphs (DAGs, also known as...
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02/12/2018
Identifiability of Nonparametric Mixture Models and Bayes Optimal Clustering
Motivated by problems in data clustering, we establish general condition...
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11/03/2017
Partial correlation graphs and the neighborhood lattice
We define and study partial correlation graphs (PCGs) with variables in ...
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03/11/2017
Learning Large-Scale Bayesian Networks with the sparsebn Package
Learning graphical models from data is an important problem with wide ap...
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11/29/2015
Learning Directed Acyclic Graphs with Penalized Neighbourhood Regression
We study a family of regularized score-based estimators for learning the...
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01/04/2014