8 citations · 31 across the 22 of their papers we have counts for
24 papers
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…
Extending Multi-Source Bayesian Optimization With Causality Principles
Luuk Jacobs, Mohammad Ali Javidian
Multi-Source Bayesian Optimization (MSBO) serves as a variant of the traditional Bayesian Optimization (BO) framework applicable to situations involving optimization of an objectiv…
Multi-Objective Multi-Fidelity Bayesian Optimization with Causal Priors
Md Abir Hossen, Mohammad Ali Javidian, Vignesh Narayanan +2
Multi-fidelity Bayesian optimization (MFBO) accelerates the search for the global optimum of black-box functions by integrating inexpensive, low-fidelity approximations. The centra…
Causally-Aware Information Bottleneck for Domain Adaptation
Mohammad Ali Javidian
We tackle a common domain adaptation setting in causal systems. In this setting, the target variable is observed in the source domain but is entirely missing in the target domain.…
An Expectation-Maximization Algorithm for Domain Adaptation in Gaussian Causal Models
Mohammad Ali Javidian
We study the problem of imputing a designated target variable that is systematically missing in a shifted deployment domain, when a Gaussian causal DAG is available from a fully ob…
Unicorn: Reasoning about Configurable System Performance through the lens of Causality
Md Shahriar Iqbal, Rahul Krishna, Mohammad Ali Javidian +2
Modern computer systems are highly configurable, with the total variability space sometimes larger than the number of atoms in the universe. Understanding and reasoning about the p…
Learning Circular Hidden Quantum Markov Models: A Tensor Network Approach
Mohammad Ali Javidian, Vaneet Aggarwal, Zubin Jacob
In this paper, we propose circular Hidden Quantum Markov Models (c-HQMMs), which can be applied for modeling temporal data in quantum datasets (with classical datasets as a special…
Quantum causal inference in the presence of hidden common causes: An entropic approach
Mohammad Ali Javidian, Vaneet Aggarwal, Zubin Jacob
Quantum causality is an emerging field of study which has the potential to greatly advance our understanding of quantum systems. In this paper, we put forth a theoretical framework…
Scalable Causal Domain Adaptation
Mohammad Ali Javidian, Om Pandey, Pooyan Jamshidi
One of the most critical problems in transfer learning is the task of domain adaptation, where the goal is to apply an algorithm trained in one or more source domains to a differen…
Accelerating Recursive Partition-Based Causal Structure Learning
Md. Musfiqur Rahman, Ayman Rasheed, Md. Mosaddek Khan +3
Causal structure discovery from observational data is fundamental to the causal understanding of autonomous systems such as medical decision support systems, advertising campaigns…
Quantum Entropic Causal Inference
Mohammad Ali Javidian, Vaneet Aggarwal, Fanglin Bao +1
The class of problems in causal inference which seeks to isolate causal correlations solely from observational data even without interventions has come to the forefront of machine…
CADET: Debugging and Fixing Misconfigurations using Counterfactual Reasoning
Rahul Krishna, Md Shahriar Iqbal, Mohammad Ali Javidian +2
Modern computing platforms are highly-configurable with thousands of interacting configurations. However, configuring these systems is challenging. Erroneous configurations can cau…
Learning LWF Chain Graphs: A Markov Blanket Discovery Approach
Mohammad Ali Javidian, Marco Valtorta, Pooyan Jamshidi
This paper provides a graphical characterization of Markov blankets in chain graphs (CGs) under the Lauritzen-Wermuth-Frydenberg (LWF) interpretation. The characterization is diffe…
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…
Order-Independent Structure Learning of Multivariate Regression Chain Graphs
Mohammad Ali Javidian, Marco Valtorta, Pooyan Jamshidi
This paper deals with multivariate regression chain graphs (MVR CGs), which were introduced by Cox and Wermuth [3,4] to represent linear causal models with correlated errors. We co…
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…
On a hypergraph probabilistic graphical model
Mohammad Ali Javidian, Linyuan Lu, Marco Valtorta +1
We propose a directed acyclic hypergraph framework for a probabilistic graphical model that we call Bayesian hypergraphs. The space of directed acyclic hypergraphs is much larger t…
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…
On the Properties of MVR Chain Graphs
Mohammad Ali Javidian, Marco Valtorta
Depending on the interpretation of the type of edges, a chain graph can represent different relations between variables and thereby independence models. Three interpretations, know…
Preventing Social Disappointment in Elections
Mohammad Ali Javidian, Pooyan Jamshidi, Marco Valtorta +1
Mechanism design is concerned with settings where a policymaker (or social planner) faces the problem of aggregating the announced preferences of multiple agents into a collective…
Disappointment in Social Choice Protocols
Mohammad Ali Javidian, Rasoul Ramezanian
Social choice theory is a theoretical framework for analysis of combining individual preferences, interests, or welfare to reach a collective decision or social welfare in some sen…