Search Results for author: Muralikrishnna G. Sethuraman

Found 4 papers, 1 papers with code

Learning Cyclic Causal Models from Incomplete Data

no code implementations23 Feb 2024 Muralikrishnna G. Sethuraman, Faramarz Fekri

Under the additive noise model, MissNODAGS learns the causal graph by alternating between imputing the missing data and maximizing the expected log-likelihood of the visible part of the data in each training step, following the principles of the expectation-maximization (EM) framework.

Causal Discovery Imputation

NODAGS-Flow: Nonlinear Cyclic Causal Structure Learning

1 code implementation4 Jan 2023 Muralikrishnna G. Sethuraman, Romain Lopez, Rahul Mohan, Faramarz Fekri, Tommaso Biancalani, Jan-Christian Hütter

Learning causal relationships between variables is a well-studied problem in statistics, with many important applications in science.

A Density Evolution framework for Preferential Recovery of Covariance and Causal Graphs from Compressed Measurements

no code implementations17 Mar 2022 Muralikrishnna G. Sethuraman, Hang Zhang, Faramarz Fekri

In this paper, we propose a general framework for designing sensing matrix $\boldsymbol{A} \in \mathbb{R}^{d\times p}$, for estimation of sparse covariance matrix from compressed measurements of the form $\boldsymbol{y} = \boldsymbol{A}\boldsymbol{x} + \boldsymbol{n}$, where $\boldsymbol{y}, \boldsymbol{n} \in \mathbb{R}^d$, and $\boldsymbol{x} \in \mathbb{R}^p$.

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