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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...
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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...
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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 ...
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02/27/2021
Scalable Causal Transfer Learning
One of the most important problems in transfer learning is the task of d...
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02/23/2021
Quantum Entropic Causal Inference
As quantum computing and networking nodes scale-up, important open quest...
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02/23/2021
Accelerating Recursive Partition-Based Causal Structure Learning
Causal structure discovery from observational data is fundamental to the...
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10/12/2020
CADET: A Systematic Method For Debugging Misconfigurations using Counterfactual Reasoning
Modern computing platforms are highly-configurable with thousands of int...
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05/29/2020
Learning LWF Chain Graphs: A Markov Blanket Discovery Approach
This paper provides a graphical characterization of Markov blankets in c...
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05/27/2020
Learning LWF Chain Graphs: an Order Independent Algorithm
LWF chain graphs combine directed acyclic graphs and undirected graphs. ...
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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...
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10/01/2019
Order-Independent Structure Learning of Multivariate Regression Chain Graphs
This paper deals with multivariate regression chain graphs (MVR CGs), wh...
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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...
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11/20/2018
On a hypergraph probabilistic graphical model
We propose a directed acyclic hypergraph framework for a probabilistic g...
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06/27/2018
Comment on: Decomposition of structural learning about directed acyclic graphs [1]
We propose an alternative proof concerning necessary and sufficient cond...
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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...
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06/03/2018
Structural Learning of Multivariate Regression Chain Graphs via Decomposition
We extend the decomposition approach for learning Bayesian networks (BN)...
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03/09/2018
On the Properties of MVR Chain Graphs
Depending on the interpretation of the type of edges, a chain graph can ...
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03/09/2018
The evolution of multivariate regression chain graphs
Depending on the interpretation of the type of edges, a chain graph can ...
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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 ...
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01/18/2018