Mohammad ali Javidian | DeepAI

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∙ 01/20/2022

Unicorn: Reasoning about Configurable System Performance through the lens of Causality

Modern computer systems are highly configurable, with the total variabil...

8 Md Shahriar Iqbal, et al. ∙

∙ 10/29/2021

Learning Circular Hidden Quantum Markov Models: A Tensor Network Approach

In this paper, we propose circular Hidden Quantum Markov Models (c-HQMMs...

0 Mohammad ali Javidian, et al. ∙

∙ 04/24/2021

Quantum Causal Inference in the Presence of Hidden Common Causes: an Entropic Approach

Quantum causality is an emerging field of study which has the potential ...

0 Mohammad ali Javidian, et al. ∙

∙ 02/27/2021

Scalable Causal Transfer Learning

One of the most important problems in transfer learning is the task of d...

13 Mohammad ali Javidian, et al. ∙

∙ 02/23/2021

Quantum Entropic Causal Inference

As quantum computing and networking nodes scale-up, important open quest...

12 Mohammad ali Javidian, et al. ∙

∙ 02/23/2021

Accelerating Recursive Partition-Based Causal Structure Learning

Causal structure discovery from observational data is fundamental to the...

15 Md. Musfiqur Rahman, et al. ∙

∙ 10/12/2020

CADET: A Systematic Method For Debugging Misconfigurations using Counterfactual Reasoning

Modern computing platforms are highly-configurable with thousands of int...

0 Rahul Krishna, et al. ∙

∙ 05/29/2020

Learning LWF Chain Graphs: A Markov Blanket Discovery Approach

This paper provides a graphical characterization of Markov blankets in c...

13 Mohammad ali Javidian, et al. ∙

∙ 05/27/2020

Learning LWF Chain Graphs: an Order Independent Algorithm

LWF chain graphs combine directed acyclic graphs and undirected graphs. ...

21 Mohammad ali Javidian, et al. ∙

∙ 02/24/2020

AMP Chain Graphs: Minimal Separators and Structure Learning Algorithms

We address the problem of finding a minimal separator in an Andersson-Ma...

7 Mohammad ali Javidian, et al. ∙

∙ 10/01/2019

Order-Independent Structure Learning of Multivariate Regression Chain Graphs

This paper deals with multivariate regression chain graphs (MVR CGs), wh...

0 Mohammad ali Javidian, et al. ∙

∙ 02/26/2019

Transfer Learning for Performance Modeling of Configurable Systems: A Causal Analysis

Modern systems (e.g., deep neural networks, big data analytics, and comp...

0 Mohammad ali Javidian, et al. ∙

∙ 11/20/2018

On a hypergraph probabilistic graphical model

We propose a directed acyclic hypergraph framework for a probabilistic g...

0 Mohammad ali Javidian, et al. ∙

∙ 06/27/2018

Comment on: Decomposition of structural learning about directed acyclic graphs [1]

We propose an alternative proof concerning necessary and sufficient cond...

0 Mohammad ali Javidian, et al. ∙

∙ 06/25/2018

A Proof of the Front-Door Adjustment Formula

We provide a proof of the the Front-Door adjustment formula using the do...

0 Mohammad ali Javidian, et al. ∙

∙ 06/03/2018

Structural Learning of Multivariate Regression Chain Graphs via Decomposition

We extend the decomposition approach for learning Bayesian networks (BN)...

0 Mohammad ali Javidian, et al. ∙

∙ 03/09/2018

On the Properties of MVR Chain Graphs

Depending on the interpretation of the type of edges, a chain graph can ...

0 Mohammad ali Javidian, et al. ∙

∙ 03/09/2018

The evolution of multivariate regression chain graphs

Depending on the interpretation of the type of edges, a chain graph can ...

0 Mohammad ali Javidian, et al. ∙

∙ 03/09/2018

The evolution of the global Markov property for multivariate regression graphs: differences and conflicts

Depending on the interpretation of the type of edges, a chain graph can ...

0 Mohammad ali Javidian, et al. ∙

∙ 01/18/2018

How can social planners prevent disappointment in an election?

Mechanism design is concerned with settings where a policy maker (or soc...

0 Rasoul Ramezanian, et al. ∙

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