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Found 9 papers, 2 papers with code
Interpretable and Fair Boolean Rule Sets via Column Generation
This paper considers the learning of Boolean rules in either disjunctive normal form (DNF, OR-of-ANDs, equivalent to decision rule sets) or conjunctive normal form (CNF, AND-of-ORs) as an interpretable model for classification.
LPRules: Rule Induction in Knowledge Graphs Using Linear Programming
A major drawback of such methods is the lack of scalability to large datasets.
AI Descartes: Combining Data and Theory for Derivable Scientific Discovery
We develop a method for combining logical reasoning with symbolic regression, enabling principled derivations of models of natural phenomena.
Integer Programming for Causal Structure Learning in the Presence of Latent Variables
The problem of finding an ancestral acyclic directed mixed graph (ADMG) that represents the causal relationships between a set of variables is an important area of research on causal inference.
Multilabel Classification by Hierarchical Partitioning and Data-dependent Grouping
We then present a hierarchical partitioning approach that exploits the label hierarchy in large scale problems to divide up the large label space and create smaller sub-problems, which can then be solved independently via the grouping approach.
Symbolic Regression using Mixed-Integer Nonlinear Optimization
The Symbolic Regression (SR) problem, where the goal is to find a regression function that does not have a pre-specified form but is any function that can be composed of a list of operators, is a hard problem in machine learning, both theoretically and computationally.
Generalized Linear Rule Models
Column generation is used to optimize over an exponentially large space of rules without pre-generating a large subset of candidates or greedily boosting rules one by one.
Boolean Decision Rules via Column Generation
This paper considers the learning of Boolean rules in either disjunctive normal form (DNF, OR-of-ANDs, equivalent to decision rule sets) or conjunctive normal form (CNF, AND-of-ORs) as an interpretable model for classification.
Globally Optimal Symbolic Regression
In this study we introduce a new technique for symbolic regression that guarantees global optimality.
