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Found 5 papers, 2 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.
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.
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.
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.
