Multi-task learning of order-consistent causal graphs

X Chen, H Sun, C Ellington, E **ng… - Advances in Neural …, 2021 - proceedings.neurips.cc
We consider the problem of discovering $ K $ related Gaussian directed acyclic graphs
(DAGs), where the involved graph structures share a consistent causal order and sparse …

uGLAD: sparse graph recovery by optimizing deep unrolled networks

H Shrivastava, U Chajewska, R Abraham… - arxiv preprint arxiv …, 2022 - arxiv.org
Probabilistic Graphical Models (PGMs) are generative models of complex systems. They rely
on conditional independence assumptions between variables to learn sparse …

A deep learning approach to recover conditional independence graphs

H Shrivastava, U Chajewska, R Abraham… - … 2022 Workshop: New …, 2022 - openreview.net
Probabilistic Graphical Models are generative models of complex systems. They rely on
conditional independence assumptions between variables to learn sparse representations …

Meta Learning for High-dimensional Ising Model Selection Using -regularized Logistic Regression

H **e, J Honorio - arxiv preprint arxiv:2208.09539, 2022 - arxiv.org
In this paper, we consider the meta learning problem for estimating the graphs associated
with high-dimensional Ising models, using the method of $\ell_1 $-regularized logistic …

uGLAD: A deep learning model to recover conditional independence graphs

H Shrivastava, U Chajewska, R Abraham, X Chen - openreview.net
Probabilistic Graphical Models are generative models of complex systems. They rely on
conditional independence assumptions between variables to learn sparse representations …