Optimization problems for machine learning: A survey
This paper surveys the machine learning literature and presents in an optimization
framework several commonly used machine learning approaches. Particularly …
framework several commonly used machine learning approaches. Particularly …
A survey on Bayesian network structure learning from data
A necessary step in the development of artificial intelligence is to enable a machine to
represent how the world works, building an internal structure from data. This structure should …
represent how the world works, building an internal structure from data. This structure should …
DAG-GNN: DAG structure learning with graph neural networks
Learning a faithful directed acyclic graph (DAG) from samples of a joint distribution is a
challenging combinatorial problem, owing to the intractable search space superexponential …
challenging combinatorial problem, owing to the intractable search space superexponential …
[PDF][PDF] Various properties of various ultrafilters, various graph width parameters, and various connectivity systems
T Fujita - arXiv preprint arXiv, 2024 - researchgate.net
This paper investigates ultrafilters in the context of connectivity systems, defined as pairs (X,
f) where X is a finite set and f is a symmetric submodular function. Ultrafilters, essential in …
f) where X is a finite set and f is a symmetric submodular function. Ultrafilters, essential in …
DAGs with no curl: An efficient DAG structure learning approach
Recently directed acyclic graph (DAG) structure learning is formulated as a constrained
continuous optimization problem with continuous acyclicity constraints and was solved …
continuous optimization problem with continuous acyclicity constraints and was solved …
[HTML][HTML] Integer linear programming for the Bayesian network structure learning problem
M Bartlett, J Cussens - Artificial Intelligence, 2017 - Elsevier
Bayesian networks are a commonly used method of representing conditional probability
relationships between a set of variables in the form of a directed acyclic graph (DAG) …
relationships between a set of variables in the form of a directed acyclic graph (DAG) …
Deep neural networks with knockoff features identify nonlinear causal relations and estimate effect sizes in complex biological systems
Background Learning the causal structure helps identify risk factors, disease mechanisms,
and candidate therapeutics for complex diseases. However, although complex biological …
and candidate therapeutics for complex diseases. However, although complex biological …
[PDF][PDF] A brief overview of applications of tree-width and other graph width parameters
T Fujita - preprint (researchgate), 2024 - researchgate.net
Graph theory, a fundamental branch of mathematics, centers on the study of networks
composed of vertices (nodes) and edges, examining their paths, structures, and properties …
composed of vertices (nodes) and edges, examining their paths, structures, and properties …
Approximate structure learning for large Bayesian networks
We present approximate structure learning algorithms for Bayesian networks. We discuss
the two main phases of the task: the preparation of the cache of the scores and structure …
the two main phases of the task: the preparation of the cache of the scores and structure …
Learning treewidth-bounded Bayesian networks with thousands of variables
M Scanagatta, G Corani… - Advances in neural …, 2016 - proceedings.neurips.cc
We present a method for learning treewidth-bounded Bayesian networks from data sets
containing thousands of variables. Bounding the treewidth of a Bayesian network greatly …
containing thousands of variables. Bounding the treewidth of a Bayesian network greatly …