no code implementations • 23 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.
1 code implementation • 4 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.
no code implementations • 17 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 $\boldsymbol{x} \in \mathbb{R}^p$.
no code implementations • 8 Nov 2021 • Muralikrishnna G. Sethuraman, Ali Payani, Faramarz Fekri, J. Clayton Kerce
To achieve this, we take a symbolic reasoning based approach using the framework of formal logic.