Derivation of Output Correlation Inferences for Multi-Output (aka Multi-Task) Gaussian Process
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Abstract (arXiv)
Gaussian process (GP) is arguably one of the most widely used machine learning algorithms in practice. One of its prominent applications is Bayesian optimization (BO). Although the vanilla GP itself is already a powerful tool for BO, it is often beneficial to be able to consider the dependencies of multiple outputs. To do so, Multi-task GP (MTGP) is formulated, but it is not trivial to fully understand the derivations of its formulations and their gradients from the previous literature. This paper serves friendly derivations of the MTGP formulations and their gradients.
Key facts. Cited by 1 paper in the map (data through 13 Oct 2026). Builds on 19 of its 42 references that are in the map. First posted on arXiv 14 Jan 2025.
Builds on
19 of 42 references.
References · 19
- 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,223
- 2018BOHB: Robust and Efficient Hyperparameter Optimization at Scalecited by 467
- 2021SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimizationcited by 229
- 2020Differentiable Expected Hypervolume Improvement for Parallel Multi-Objective Bayesian Optimizationcited by 177
- 2023Tree-Structured Parzen Estimator: Understanding Its Algorithm Components and Their Roles for Better Empirical Performancecited by 144
- 2020Efficiently Sampling Functions from Gaussian Process Posteriorscited by 126
- 2017Multi-fidelity Bayesian Optimisation with Continuous Approximationscited by 120
- 2014Bayesian Optimization with Unknown Constraintscited by 109
- 2021Multi-Objective Bayesian Optimization over High-Dimensional Search Spacescited by 87
- 2020Scalable Constrained Bayesian Optimizationcited by 84
- 2018A General Framework for Multi-fidelity Bayesian Optimization with Gaussian Processescited by 52
- 2017Scalable Log Determinants for Gaussian Process Kernel Learningcited by 34
7 more
- 2021Bayesian Optimization with High-Dimensional Outputscited by 32
- 2016Multi-fidelity Gaussian Process Bandit Optimisationcited by 31
- 2018Practical Transfer Learning for Bayesian Optimizationcited by 26
- 2022Supervising the Multi-Fidelity Race of Hyperparameter Configurationscited by 15
- 2022c-TPE: Tree-structured Parzen Estimator with Inequality Constraints for Expensive Hyperparameter Optimizationcited by 9
- 2024Scaling Gaussian Processes for Learning Curve Prediction via Latent Kronecker Structurecited by 9
- 2022Speeding Up Multi-Objective Hyperparameter Optimization by Task Similarity-Based Meta-Learning for the Tree-Structured Parzen Estimatorcited by 7
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