Information-Theoretic Multi-Objective Bayesian Optimization with Continuous Approximations

Related tags

Deep LearningiMOCA
Overview

Information-Theoretic Multi-Objective Bayesian Optimization with Continuous Approximations

Requirements

The code is implemented in Python and requires the following packages:

  1. sobol_seq

  2. platypus

  3. sklearn.gaussian_process

Citation

If you use this code in your academic work please cite our JAIR paper: "A General Output Space Entropy Search Framework for Multi-Objective Bayesian Optimization " and our workshop paper "Information-Theoretic Multi-Objective Bayesian Optimization with Continuous Approximations" , Syrine Belakaria, Aryan Deshwal, Janardhan Rao Doppa.

Owner
GitHub Repository
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