Article2014

Bayesian Optimization with Inequality Constraints

Jacob R. Gardner · Matt J. Kusner · Zhixiang · Kilian Q. Weinberger · John Patrick Cunningham

347 citations0/0 files stored

No readable copy stored yet — this record is metadata only.

No open-access location is known for this record yet.

Literature note Cite in a project Citation map

Advanced Bandit Algorithms Research Advanced Multi-Objective Optimization Algorithms Gaussian Processes and Bayesian Inference

Abstract

Bayesian optimization is a powerful framework for minimizing expensive objective functions while using very few function evaluations. It has been successfully applied to a variety of prob-lems, including hyperparameter tuning and ex-perimental design. However, this framework has not been extended to the inequality-constrained optimization setting, particularly the setting in which evaluating feasibility is just as expensive as evaluating the objective. Here we present con-strained Bayesian optimization, which places a prior distribution on both the objective and the constraint functions. We evaluate our method on simulated and real data, demonstrating that con-strained Bayesian optimization can quickly find optimal and feasible points, even when small fea-sible regions cause standard methods to fail. 1.

Paper brief

Loading…

Cite this work

Citation unavailable for this work.

BibTeX RIS CSL-JSON

No public copy is stored for this record, so only the metadata is cited.

Citations in this library

Citation map →

Stored only when both papers are in the corpus — 347 citations exist upstream.

References

2012Practical Bayesian Optimization of Machine Learning Algorithms 2010A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning 2013Multi-Task Bayesian Optimization 2016Bayesian Optimization in a Billion Dimensions via Random Embeddings

Cited by

2018Constrained Bayesian Optimization with Noisy Experiments 2021Bayesian Optimization is Superior to Random Search for Machine Learning Hyperparameter Tuning: Analysis of the Black-Box Optimization Challenge 2020 2018A Global Bayesian Optimization Algorithm and Its Application to Integrated System Design 2015A General Framework for Constrained Bayesian Optimization using Information-based Search 2015Predictive Entropy Search for Bayesian Optimization with Unknown Constraints 2016Bayesian Optimization with Exponential Convergence 2019A Framework for Bayesian Optimization in Embedded Subspaces 2021Fair Bayesian Optimization

Explore the map →

Papers that cite the same references — the relational fallback while embeddings are still backfilling.

Imported from OpenAlex (CC0) — nothing here is generated.