216 citations · 454 across the 55 of their papers we have counts for
16 papers · 1 filter
Intervening to Learn and Compose Causally Disentangled Representations
Alex Markham, Isaac Hirsch, Jeri A. Chang +2
In designing generative models, it is commonly believed that in order to learn useful latent structure, we face a fundamental tension between expressivity and structure. In this pa…
Towards Interpretable Deep Generative Models via Causal Representation Learning
Gemma E. Moran, Bryon Aragam
Recent developments in generative artificial intelligence (AI) rely on machine learning techniques such as deep learning and generative modeling to achieve state-of-the-art perform…
Markov Equivalence and Consistency in Differentiable Structure Learning
Chang Deng, Kevin Bello, Pradeep Ravikumar +1
Existing approaches to differentiable structure learning of directed acyclic graphs (DAGs) rely on strong identifiability assumptions in order to guarantee that global minimizers o…
Dimension-independent rates for structured neural density estimation
Robert A. Vandermeulen, Wai Ming Tai, Bryon Aragam
We show that deep neural networks achieve dimension-independent rates of convergence for learning structured densities such as those arising in image, audio, video, and text applic…
Breaking the curse of dimensionality in structured density estimation
Robert A. Vandermeulen, Wai Ming Tai, Bryon Aragam
We consider the problem of estimating a structured multivariate density, subject to Markov conditions implied by an undirected graph. In the worst case, without Markovian assumptio…
Greedy equivalence search for nonparametric graphical models
Bryon Aragam
One of the hallmark achievements of the theory of graphical models and Bayesian model selection is the celebrated greedy equivalence search (GES) algorithm due to Chickering and Me…
Learning nonparametric latent causal graphs with unknown interventions
Yibo Jiang, Bryon Aragam
We establish conditions under which latent causal graphs are nonparametrically identifiable and can be reconstructed from unknown interventions in the latent space. Our primary foc…
Optimizing NOTEARS Objectives via Topological Swaps
Chang Deng, Kevin Bello, Bryon Aragam +1
Recently, an intriguing class of non-convex optimization problems has emerged in the context of learning directed acyclic graphs (DAGs). These problems involve minimizing a given l…
Neuro-Causal Factor Analysis
Alex Markham, Mingyu Liu, Bryon Aragam +1
Factor analysis (FA) is a statistical method for explaining how mutually dependent observed variables can be represented in terms of mutually independent latent factors, and it is…
NOTMAD: Estimating Bayesian Networks with Sample-Specific Structures and Parameters
Ben Lengerich, Caleb Ellington, Bryon Aragam +2
Context-specific Bayesian networks (i.e. directed acyclic graphs, DAGs) identify context-dependent relationships between variables, but the non-convexity induced by the acyclicity…
A polynomial-time algorithm for learning nonparametric causal graphs
Ming Gao, Yi Ding, Bryon Aragam
We establish finite-sample guarantees for a polynomial-time algorithm for learning a nonlinear, nonparametric directed acyclic graphical (DAG) model from data. The analysis is mode…
DYNOTEARS: Structure Learning from Time-Series Data
Roxana Pamfil, Nisara Sriwattanaworachai, Shaan Desai +4
We revisit the structure learning problem for dynamic Bayesian networks and propose a method that simultaneously estimates contemporaneous (intra-slice) and time-lagged (inter-slic…
Learning Sample-Specific Models with Low-Rank Personalized Regression
Benjamin Lengerich, Bryon Aragam, Eric P. Xing
Modern applications of machine learning (ML) deal with increasingly heterogeneous datasets comprised of data collected from overlapping latent subpopulations. As a result, traditio…
Learning Sparse Nonparametric DAGs
Xun Zheng, Chen Dan, Bryon Aragam +2
We develop a framework for learning sparse nonparametric directed acyclic graphs (DAGs) from data. Our approach is based on a recent algebraic characterization of DAGs that led to…
DAGs with NO TEARS: Continuous Optimization for Structure Learning
Xun Zheng, Bryon Aragam, Pradeep Ravikumar +1
Estimating the structure of directed acyclic graphs (DAGs, also known as Bayesian networks) is a challenging problem since the search space of DAGs is combinatorial and scales supe…
Learning Large-Scale Bayesian Networks with the sparsebn Package
Bryon Aragam, Jiaying Gu, Qing Zhou
Learning graphical models from data is an important problem with wide applications, ranging from genomics to the social sciences. Nowadays datasets often have upwards of thousands-…