8 citations · 31 across the 22 of their papers we have counts for
7 papers · 1 filter
Transferring Information Across Interventions in Causal Bayesian Optimization
Mohammad Ali Javidian
Bayesian optimization is a popular way to optimize expensive systems, where every experiment, simulation, or intervention costs time or money. In its standard form, it treats the v…
Learning LWF Chain Graphs: an Order Independent Algorithm
Mohammad Ali Javidian, Marco Valtorta, Pooyan Jamshidi
LWF chain graphs combine directed acyclic graphs and undirected graphs. We present a PC-like algorithm that finds the structure of chain graphs under the faithfulness assumption to…
AMP Chain Graphs: Minimal Separators and Structure Learning Algorithms
Mohammad Ali Javidian, Marco Valtorta, Pooyan Jamshidi
We address the problem of finding a minimal separator in an Andersson-Madigan-Perlman chain graph (AMP CG), namely, finding a set Z of nodes that separates a given nonadjacent pair…
Transfer Learning for Performance Modeling of Configurable Systems: A Causal Analysis
Mohammad Ali Javidian, Pooyan Jamshidi, Marco Valtorta
Modern systems (e.g., deep neural networks, big data analytics, and compilers) are highly configurable, which means they expose different performance behavior under different confi…
Comment on: Decomposition of structural learning about directed acyclic graphs [1]
Mohammad Ali Javidian, Marco Valtorta
We propose an alternative proof concerning necessary and sufficient conditions to split the problem of searching for d-separators and building the skeleton of a DAG into small prob…
A Proof of the Front-Door Adjustment Formula
Mohammad Ali Javidian, Marco Valtorta
We provide a proof of the the Front-Door adjustment formula using the do-calculus.
Structural Learning of Multivariate Regression Chain Graphs via Decomposition
Mohammad Ali Javidian, Marco Valtorta
We extend the decomposition approach for learning Bayesian networks (BNs) proposed by (Xie et. al.) to learning multivariate regression chain graphs (MVR CGs), which include BNs as…