Mohammad Ali Javidian

Mohammad Ali Javidian
Appalachian State University | ASU · Department of Computer Science

PhD
Assistant Professor

About

27
Publications
2,568
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32
Citations
Introduction
I am a postdoctoral scholar at the Electrodynamics Lab and CLAN Lab at Purdue University, where I investigate the development of novel algorithmic and theoretically principled methods for Quantum Entropic Causal Inference. Before this current position, I was a research associate at the AISys Lab at the Department of Computer Science and Engineering of the University of South Carolina, where I conducted research projects on transfer learning and performance debugging in machine learning systems.

Publications

Publications (27)
Preprint
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Modern computer systems are highly configurable, with the variability space sometimes larger than the number of atoms in the universe. Understanding and reasoning about the performance behavior of highly configurable systems, due to a vast variability space, is challenging. State-of-the-art methods for performance modeling and analyses rely on pred...
Preprint
Full-text available
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 case). We show that c-HQMMs are equivalent to a constrained tensor network (more precisely, circular Local Purified State with positive-semidefinite decomposition) model...
Article
We extend the decomposition approach for learning Bayesian networks (BNs) proposed by Xie et al. (2006) [55] to learning multivariate regression chain graphs (MVR CGs), which include BNs as a special case. The same advantages of this decomposition approach hold in the more general setting: reduced complexity and increased power of computational ind...
Preprint
Full-text available
Quantum causality is an emerging field of study which has the potential to greatly advance our understanding of quantum systems. One of the most important problems in quantum causality is linked to this prominent aphorism that states correlation does not mean causation. A direct generalization of the existing causal inference techniques to the quan...
Article
LWF chain graphs combine directed acyclic graphs and undirected graphs. We propose a PC-like algorithm, called PC4LWF, that finds the structure of chain graphs under the faithfulness assumption to resolve the problem of scalability of the proposed algorithm by Studeny (1997). We prove that PC4LWF is order dependent, in the sense that the output can...
Preprint
Full-text available
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 different (but related) target domain. This paper deals with domain adaptation in the presence of covariate shift while invariances exist across domains. One of the main limitati...
Preprint
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Causal structure discovery from observational data is fundamental to the causal understanding of autonomous systems such as medical decision support systems, advertising campaigns and self-driving cars. This is essential to solve well-known causal decision making and prediction problems associated with those real-world applications. Recently, recur...
Preprint
Full-text available
As quantum computing and networking nodes scale-up, important open questions arise on the causal influence of various sub-systems on the total system performance. These questions are related to the tomographic reconstruction of the macroscopic wavefunction and optimizing connectivity of large engineered qubit systems, the reliable broadcasting of i...
Conference Paper
Inferring causality from observational data alone is one of the most important and challenging problems in statistical inference. We propose a greedy algorithm for quantum entropic causal inference that unifies classical and quantum causal inference.
Preprint
Full-text available
Modern computing platforms are highly-configurable with thousands of interacting configurations. However, configuring these systems is challenging. Erroneous configurations can cause unexpected non-functional faults. This paper proposes CADET (short for Causal Debugging Toolkit) that enables users to identify, explain, and fix the root cause of non...
Article
This paper deals with chain graphs (CGs) under the Andersson–Madigan–Perlman (AMP) interpretation. We address the problem of finding a minimal separator in an AMP CG, namely, finding a set Z of nodes that separates a given non-adjacent pair of nodes such that no proper subset of Z separates that pair. We analyze several versions of this problem and...
Article
Full-text available
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 than the space of chain graphs. Hence Bayesian hypergraphs can model much finer factorizations than Bayesian networks or LWF chain graphs and provide simpler and more comp...
Preprint
Full-text available
This paper provides a graphical characterization of Markov blankets in chain graphs (CGs) under the Lauritzen-Wermuth-Frydenberg (LWF) interpretation. The characterization is different from the well-known one for Bayesian networks and generalizes it. We provide a novel scalable and sound algorithm for Markov blanket discovery in LWF CGs and prove t...
Preprint
Full-text available
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 resolve the problem of scalability of the proposed algorithm by Studeny (1997). We prove that our PC-like algorithm is order dependent, in the sense that the output can...
Preprint
Full-text available
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 separate a given non-adjacent pair of nodes such that no proper subset of Z separates that pair. We analyze several versions of this problem and offer polynomial-time algorithms for each. These include fi...
Chapter
This paper deals with multivariate regression chain graphs (MVR CGs), which were introduced by Cox and Wermuth in the nineties to represent linear causal models with correlated errors. We consider the PC-like algorithm for structure learning of MVR CGs, a constraint-based method proposed by Sonntag and Peña in 2012. We show that the PC-like algorit...
Preprint
Full-text available
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 consider the PC-like algorithm for structure learning of MVR CGs, which is a constraint-based method proposed by Sonntag and Pe\~{n}a in [18]. We show that the PC-like algo...
Preprint
Full-text available
Modern systems (e.g., deep neural networks, big data analytics, and compilers) are highly configurable, which means they expose different performance behavior under different configurations. The fundamental challenge is that one cannot simply measure all configurations due to the sheer size of the configuration space. Transfer learning has been use...
Preprint
Full-text available
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 than the space of chain graphs. Hence Bayesian hypergraphs can model much finer factorizations than Bayesian networks or LWF chain graphs and provide simpler and more comp...
Conference Paper
We address the problem of finding a minimal separator in a LWF chain graph, namely, finding a set Z of nodes that separates a given non-adjacent pair of nodes such that no proper subset of Z separates that pair. We analyze several versions of this problem and offer polynomial time algorithms for each. These include finding a minimal separator from...
Conference Paper
Full-text available
We address a problem with potential applications in learning and causal inference: finding a minimal separator in a (maximal) ancestral graph, namely, finding a set Z of nodes that separates a given non-adjacent pair of nodes such that no proper subset of Z separates that pair. We analyze several versions of this problem and offer polynomial time a...
Preprint
Full-text available
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 problems for every node of a separation tree T. The proof is simpler than the original [1]. The same proof structure has been used in [2] for learning the structure of multiv...
Preprint
Full-text available
We provide a proof of the the Front-Door adjustment formula using the do-calculus.
Preprint
Full-text available
We extend the decomposition approach for learning Bayesian networks (BN) proposed by (Xie et al., 2006) to learning multivariate regression chain graphs (MVR CGs), which include BNs as a special case. The same advantages of this decomposition approach hold in the more general setting: reduced complexity and increased power of computational independ...
Article
Full-text available
Depending on the interpretation of the type of edges, a chain graph can represent different relations between variables and thereby independence models. Three interpretations, known by the acronyms LWF, MVR, and AMP are prevalent. We review Markov properties for MVR chain graphs and propose an alternative local Markov property for them. Except for...
Article
Full-text available
Mechanism design is concerned with settings where a policy maker (or social planner) faces the problem of aggregating the announced preferences of multiple agents into a collective (or social), system-wide decision. One of the most important ways for aggregation preference used in a multi agent system is using election. In an election, the aim is t...

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Projects (2)
Project
proposing novel algorithms for learning the structure of causal models in order to answer the following questions: How to use the causal structure of machine learning systems for identifying and estimating causal effects of configuration options on performance? How to apply transfer learning for performance analysis of machine learning systems by means of causal models? How to use causal inference tools for performance debugging and explainability in machine learning systems?