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Found 12 papers, 4 papers with code
Obtaining Explainable Classification Models using Distributionally Robust Optimization
Model explainability is crucial for human users to be able to interpret how a proposed classifier assigns labels to data based on its feature values.
AI Hilbert: A New Paradigm for Scientific Discovery by Unifying Data and Background Knowledge
The discovery of scientific formulae that parsimoniously explain natural phenomena and align with existing background theory is a key goal in science.
Bayesian Experimental Design for Symbolic Discovery
This study concerns the formulation and application of Bayesian optimal experimental design to symbolic discovery, which is the inference from observational data of predictive models taking general functional forms.
Interpretable and Fair Boolean Rule Sets via Column Generation
This paper considers the learning of Boolean rules in disjunctive normal form (DNF, OR-of-ANDs, equivalent to decision rule sets) as an interpretable model for classification.
Rule Induction in Knowledge Graphs Using Linear Programming
We present a simple linear programming (LP) based method to learn compact and interpretable sets of rules encoding the facts in a knowledge graph (KG) and use these rules to solve the KG completion problem.
AI Descartes: Combining Data and Theory for Derivable Scientific Discovery
We develop a method to enable principled derivations of models of natural phenomena from axiomatic knowledge and experimental data by combining logical reasoning with symbolic regression.
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.
