Search Results for author:
Found 13 papers, 5 papers with code
Advances in Automatically Rating the Trustworthiness of Text Processing Services
Then, we will outline challenges and vision for a principled, multi-modal, causality-based rating methodologies and its implication for decision-support in real-world scenarios like health and food recommendation.
Rating Sentiment Analysis Systems for Bias through a Causal Lens
Sentiment Analysis Systems (SASs) are data-driven Artificial Intelligence (AI) systems that, given a piece of text, assign one or more numbers conveying the polarity and emotional intensity expressed in the input.
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.
A New Result on the Complexity of Heuristic Estimates for the A* Algorithm
In this paper, we review the traditional definition of problem relaxation and show that searching in the abstraction hierarchy created by problem relaxation will not reduce the computational effort required to find optimal solutions to the base- level problem, unless the relaxed problem found in the hierarchy can be transformed by some optimization (e. g., subproblem factoring).
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.
