3 citations · 3 across the 5 of their papers we have counts for
8 papers
SCOUT: Cyclic Causal Discovery Under Soft Interventions with Unknown Targets
Alpar Turkoglu, Muralikrishnna G. Sethuraman, Faramarz Fekri
Learning causal relationships between variables from data is a fundamental research area with many applications across disciplines. Most existing causal discovery algorithms rely o…
RECLAIM: Cyclic Causal Discovery Amid Measurement Noise
Muralikrishnna G. Sethuraman, Faramarz Fekri
Uncovering causal relationships is a fundamental problem across science and engineering. However, most existing causal discovery methods assume acyclicity and direct access to the…
Differentiable Cyclic Causal Discovery Under Unmeasured Confounders
Muralikrishnna G. Sethuraman, Faramarz Fekri
Understanding causal relationships between variables is fundamental across scientific disciplines. Most causal discovery algorithms rely on two key assumptions: (i) all variables a…
MissNODAG: Differentiable Cyclic Causal Graph Learning from Incomplete Data
Muralikrishnna G. Sethuraman, Razieh Nabi, Faramarz Fekri
Causal discovery in real-world systems, such as biological networks, is often complicated by feedback loops and incomplete data. Standard algorithms, which assume acyclic structure…
Learning Cyclic Causal Models from Incomplete Data
Muralikrishnna G. Sethuraman, Faramarz Fekri
Causal learning is a fundamental problem in statistics and science, offering insights into predicting the effects of unseen treatments on a system. Despite recent advances in this…
NODAGS-Flow: Nonlinear Cyclic Causal Structure Learning
Muralikrishnna G. Sethuraman, Romain Lopez, Rahul Mohan +3
Learning causal relationships between variables is a well-studied problem in statistics, with many important applications in science. However, modeling real-world systems remain ch…
A Density Evolution framework for Preferential Recovery of Covariance and Causal Graphs from Compressed Measurements
Muralikrishnna G. Sethuraman, Hang Zhang, Faramarz Fekri
In this paper, we propose a general framework for designing sensing matrix , for estimation of sparse covariance matrix from compressed m…
Visual Question Answering based on Formal Logic
Muralikrishnna G. Sethuraman, Ali Payani, Faramarz Fekri +1
Visual question answering (VQA) has been gaining a lot of traction in the machine learning community in the recent years due to the challenges posed in understanding information co…