2 citations · 3 across the 8 of their papers we have counts for
4 papers · 1 filter
SVI-DAG: A Structured Variational Inference Approach to Bayesian Causal Discovery
Shrenik Zinage
Bayesian causal discovery seeks to determine the posterior distribution of causal theories, which are interpreted as directed acyclic graphs (DAGs) that explain the observed data.…
Bayesian Calibration of Engine-out NOx Models for Engine-to-Engine Transferability
Shrenik Zinage, Peter Meckl, Ilias Bilionis
Accurate prediction of engine-out NOx is essential for meeting stringent emissions regulations and optimizing engine performance. Traditional approaches rely on models trained on d…
A Causal Graph-Enhanced Gaussian Process Regression for Modeling Engine-out NOx
Shrenik Zinage, Ilias Bilionis, Peter Meckl
The stringent regulatory requirements on nitrogen oxides (NOx) emissions from diesel compression ignition engines require accurate and reliable models for real time monitoring and…
DKL-KAN: Scalable Deep Kernel Learning using Kolmogorov-Arnold Networks
Shrenik Zinage, Sudeepta Mondal, Soumalya Sarkar
The need for scalable and expressive models in machine learning is paramount, particularly in applications requiring both structural depth and flexibility. Traditional deep learnin…