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Found 16 papers, 7 papers with code
Unicorn: Reasoning about Configurable System Performance through the lens of Causality
Understanding and reasoning about the performance behavior of highly configurable systems, over a vast and variable space, is challenging.
Learning Circular Hidden Quantum Markov Models: A Tensor Network Approach
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).
Quantum causal inference in the presence of hidden common causes: An entropic approach
We also demonstrate that the proposed approach outperforms the results of classical causal inference for the Tubingen database when the variables are classical by exploiting quantum dependence between variables through density matrices rather than joint probability distributions.
Scalable Causal Domain Adaptation
To overcome this difficulty, we propose SCTL, an algorithm that avoids an exhaustive search and identifies invariant causal features across source and target domains based on Markov blanket discovery.
Accelerating Recursive Partition-Based Causal Structure Learning
This paper proposes a generic causal structure refinement strategy that can locate the undesired relations with a small number of CI-tests, thus speeding up the algorithm for large and complex problems.
Quantum Entropic Causal Inference
This successful inference on a synthetic quantum dataset can have practical applications in identifying originators of malicious activity on future multi-node quantum networks as well as quantum error correction.
Learning LWF Chain Graphs: A Markov Blanket Discovery Approach
We provide a novel scalable and sound algorithm for Markov blanket discovery in LWF CGs and prove that the Grow-Shrink algorithm, the IAMB algorithm, and its variants are still correct for Markov blanket discovery in LWF CGs under the same assumptions as for Bayesian networks.
Learning LWF Chain Graphs: an Order Independent Algorithm
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).
AMP Chain Graphs: Minimal Separators and Structure Learning Algorithms
To address the problem of learning the structure of AMP CGs from data, we show that the PC-like algorithm (Pena, 2012) is order-dependent, in the sense that the output can depend on the order in which the variables are given.
Order-Independent Structure Learning of Multivariate Regression Chain Graphs
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].
Transfer Learning for Performance Modeling of Configurable Systems: A Causal Analysis
We expect that the ability to carry over causal relations will enable effective performance analysis of highly-configurable systems.
On a hypergraph probabilistic graphical model
We propose a directed acyclic hypergraph framework for a probabilistic graphical model that we call Bayesian hypergraphs.
Comment on: Decomposition of structural learning about directed acyclic graphs [1]
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].
A Proof of the Front-Door Adjustment Formula
We provide a proof of the the Front-Door adjustment formula using the do-calculus.
Structural Learning of Multivariate Regression Chain Graphs via Decomposition
We extend the decomposition approach for learning Bayesian networks (BNs) proposed by (Xie et.
On the Properties of MVR Chain Graphs
Except for pairwise Markov properties, we show that for MVR chain graphs all Markov properties in the literature are equivalent for semi-graphoids.
