216 citations · 454 across the 55 of their papers we have counts for
5 papers · 1 filter
DAGMA: Learning DAGs via M-matrices and a Log-Determinant Acyclicity Characterization
Kevin Bello, Bryon Aragam, Pradeep Ravikumar
The combinatorial problem of learning directed acyclic graphs (DAGs) from data was recently framed as a purely continuous optimization problem by leveraging a differentiable acycli…
Identifiability of deep generative models without auxiliary information
Bohdan Kivva, Goutham Rajendran, Pradeep Ravikumar +1
We prove identifiability of a broad class of deep latent variable models that (a) have universal approximation capabilities and (b) are the decoders of variational autoencoders tha…
Learning general conditional independence structures via the neighbourhood lattice
Arash A. Amini, Bryon Aragam, Qing Zhou
We study the problem of learning multivariate dependencies in nonparametric and high-dimensional settings. This includes but is not limited to graphical models. Our approach effect…
Tight Bounds on the Hardness of Learning Simple Nonparametric Mixtures
Bryon Aragam, Wai Ming Tai
We study the problem of learning nonparametric distributions in a finite mixture, and establish tight bounds on the sample complexity for learning the component distributions in su…
Optimal estimation of Gaussian DAG models
Ming Gao, Wai Ming Tai, Bryon Aragam
We study the optimal sample complexity of learning a Gaussian directed acyclic graph (DAG) from observational data. Our main results establish the minimax optimal sample complexity…