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Analysis of Multi-objective Bayesian Optimization Using Random Scalarizations for Correlated Observations

Hiroshi Ohno
2021 SN Computer Science  
Bayesian optimization (BO) has been used for a wide range of applications.  ...  For multi-objective BO (MOBO) using random scalarizations (linear and Tchebychev schemes) and vector-valued GPs, regret bounds are analyzed.  ...  Kazutoshi Miwa for helpful discussions about hydrogen storage materials.  ... 
doi:10.1007/s42979-021-00505-y fatcat:fm74qnldn5adxb6xun626lhi74

Multi-objective Bayesian Optimization with Heuristic Objectives for Biomedical and Molecular Data Analysis Workflows [article]

Alina Selega, Kieran R Campbell
2022 bioRxiv   pre-print
Many practical applications require optimization of multiple, computationally expensive, and possibly competing objectives that are well-suited for multi-objective Bayesian optimization (MOBO) procedures  ...  However, for many types of biomedical data, measures of data analysis workflow success are often heuristic and therefore it is not known a priori which objectives are useful.  ...  Multi-objective Bayesian optimization over heuristic objectives Setup.  ... 
doi:10.1101/2022.06.08.495370 fatcat:okfzdcm7ajbqhox63kpnjrfwby

Random Hypervolume Scalarizations for Provable Multi-Objective Black Box Optimization [article]

Daniel Golovin, Qiuyi Zhang
2020 arXiv   pre-print
Furthermore, we highlight the general utility of our scalarization framework by showing that any provably convergent single-objective optimization process can be effortlessly converted to a multi-objective  ...  In this paper, we introduce a novel scalarization function, which we term the hypervolume scalarization, and show that drawing random scalarizations from an appropriately chosen distribution can be used  ...  Due to the difficulty of using the hypervolume directly, many multi-objective optimization problems use a heuristic-based scalarization strategy, which splits the multi-objective optimization into numerous  ... 
arXiv:2006.04655v2 fatcat:fccjcytxy5cgpom4gbn43y4m2m

Lost in Optimization of Water Distribution Systems: Better Call Bayes

Antonio Candelieri, Andrea Ponti, Ilaria Giordani, Francesco Archetti
2022 Water  
BO can also be extended to multi-objective optimization. Two algorithms are proposed for multi-objective detection problems using two different acquisition functions.  ...  These characteristics of the Bayesian optimization approach are exemplified by two problems: cost/energy optimization in pump scheduling and optimal sensor placement for early detection of contaminant  ...  Acknowledgments: We greatly acknowledge the DEMS Data Science Lab for supporting this work by providing computational resources (DEMS-Department of Economics, Management and Statistics).  ... 
doi:10.3390/w14050800 fatcat:kvqawljdcfdbzi5us6ymtuztam

A survey on multi-objective hyperparameter optimization algorithms for Machine Learning [article]

Alejandro Morales-Hernández and Inneke Van Nieuwenhuyse and Sebastian Rojas Gonzalez
2021 arXiv   pre-print
We also discuss the quality metrics used to compare multi-objective HPO procedures and present future research directions.  ...  , and approaches using a mixture of both.  ...  GPs use a covariance function, or kernel, to compute the spatial correlation among several output observations for a given performance measure (i.e., a given objective of the HPO algorithm; see Figure  ... 
arXiv:2111.13755v2 fatcat:q2qtofihtzev5mose5aj7odfzm

Robust Multi-Objective Bayesian Optimization Under Input Noise [article]

Samuel Daulton, Sait Cakmak, Maximilian Balandat, Michael A. Osborne, Enlu Zhou, Eytan Bakshy
2022 arXiv   pre-print
Since directly optimizing MVaR is computationally infeasible in many settings, we propose a scalable, theoretically-grounded approach for optimizing MVaR using random scalarizations.  ...  Bayesian optimization (BO) is a sample-efficient approach for tuning design parameters to optimize expensive-to-evaluate, black-box performance metrics.  ...  In addition, Sait Cakmak and Enlu Zhou are grateful for support by the Air Force Office of Scientific Research under Grant FA9550-19-1-0283.  ... 
arXiv:2202.07549v4 fatcat:2y2sby5jtjcbvldqd7yizxmd5m

Multi-layer graph analytics for social networks

Brandon Oselio, Alex Kulesza, Alfred O. Hero
2013 2013 5th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)  
The resulting mixture can be viewed as a scalarization of a multi-objective optimization problem [2], [3], [4] .  ...  We then step back from the Bayesian setting and discuss how multi-objective optimization can be used to perform MAP estimation of the desired latent variables.  ...  ACKNOWLEDGEMENTS We would like to thank Kevin Xu for providing the code for the DSBM model and his suggestions for utilizing it, as well as his general comments on the content of the paper.  ... 
doi:10.1109/camsap.2013.6714063 dblp:conf/camsap/OselioKH13 fatcat:murl4cojqndafdpzehav3u7wcm

Multi-objective Asynchronous Successive Halving [article]

Robin Schmucker, Michele Donini, Muhammad Bilal Zafar, David Salinas, Cédric Archambeau
2021 arXiv   pre-print
Further, we observe that that taking the entire Pareto front into account for candidate selection consistently outperforms multi-fidelity HPO based on MO scalarization in terms of wall-clock time.  ...  However, in a plethora of real-world applications, accuracy is only one of the multiple -- often conflicting -- performance criteria, necessitating the adoption of a multi-objective (MO) perspective.  ...  use of multi-fidelity optimization for multi-objective HPO.  ... 
arXiv:2106.12639v1 fatcat:tny46guv2rhehgpbinhks6bgou

CAD Tool Design Space Exploration via Bayesian Optimization [article]

Yuzhe Ma, Ziyang Yu, Bei Yu
2019 arXiv   pre-print
It is based on Bayesian optimization which is a promising technique for optimizing black-box functions that are expensive to evaluate.  ...  Gaussian process regression is leveraged as the surrogate model in Bayesian optimization framework. In this work, we use 64-bit prefix adder design as a case study.  ...  ACKNOWLEDGMENTS This work is supported by The Research Grants Council of Hong Kong SAR (Project No. CUHK24209017).  ... 
arXiv:1912.06460v1 fatcat:xjyxzxg3jrecdpk4eoanjvbfzu

Constrained multi-objective optimization of process design parameters in settings with scarce data: an application to adhesive bonding [article]

Alejandro Morales-Hernández, Sebastian Rojas Gonzalez, Inneke Van Nieuwenhuyse, Jeroen Jordens, Maarten Witters, Bart Van Doninck
2022 arXiv   pre-print
Finding the optimal process parameters for an adhesive bonding process is challenging: the optimization is inherently multi-objective (aiming to maximize break strength while minimizing cost) and constrained  ...  Adhesive joints are increasingly used in industry for a wide variety of applications because of their favorable characteristics such as high strength-to-weight ratio, design flexibility, limited stress  ...  In this article, we illustrate the power of a Bayesian optimization approach for the multi-objective optimization of a novel adhesive bonding process.  ... 
arXiv:2112.08760v2 fatcat:p4oqsuyprrdm7e3mrze2yrcfqu

On the performance of multi-objective estimation of distribution algorithms for combinatorial problems [article]

Marcella S. R. Martins, Mohamed El Yafrani, Roberto Santana, Myriam Delgado, Ricardo Lüders, Belaïd Ahiod
2018 arXiv   pre-print
In this work, a fitness landscape analysis using problem features is performed for a Multi-objective Bayesian Optimization Algorithm (mBOA) on instances of MNK-landscape problem for 2, 3, 5 and 8 objectives  ...  Fitness landscape analysis investigates features with a high influence on the performance of optimization algorithms, aiming to take advantage of the addressed problem characteristics.  ...  Arnaud Liefooghe for his insightful feedback and assistance. M. Delgado acknowledges CNPq grant 309197/2014-7. R.  ... 
arXiv:1806.09935v1 fatcat:v5mexqj4zbbunodjdhvhajp6mq

Recent Advances in Bayesian Optimization [article]

Xilu Wang, Yaochu Jin, Sebastian Schmitt, Markus Olhofer
2022 arXiv   pre-print
Bayesian optimization has emerged at the forefront of expensive black-box optimization due to its data efficiency.  ...  Recent years have witnessed a proliferation of studies on the development of new Bayesian optimization algorithms and their applications.  ...  For example, in MOEA/D-EGO [224] , Tchebycheff scalarizing function is used to decompose an MOP into a set of single-objective subproblems.  ... 
arXiv:2206.03301v1 fatcat:d4mlbxwdjvad5jbzmsskp44dxq

Multi-Objective Bayesian Optimization over High-Dimensional Search Spaces [article]

Samuel Daulton, David Eriksson, Maximilian Balandat, Eytan Bakshy
2022 arXiv   pre-print
When the objectives are expensive-to-evaluate, multi-objective Bayesian optimization (BO) is a popular approach because of its high sample efficiency.  ...  scale cubically with the number of observations.  ...  In the multi-objective setting, a common approach is to optimize random scalarizations of the objectives [Knowles, 2006 , Paria et al., 2020 ] using a single-objective acquisition function.  ... 
arXiv:2109.10964v4 fatcat:g4bje3y7wrfvfkbgr3ve2p7xqq

Bayesian network model of overall print quality: Construction and structural optimisation

Tuomas Eerola, Lasse Lensu, Joni-Kristian Kamarainen, Tuomas Leisti, Risto Ritala, Göte Nyman, Heikki Kälviäinen
2011 Pattern Recognition Letters  
In this work, a computational model for representing and quantifying the overall visual quality of prints is proposed. The * Corresponding author.  ...  The model reveals and represents the explanatory factors between its elements providing insight to the psychophysical phenomenon of how observers perceive visual quality and which measurable entities affect  ...  Acknowledgement The authors would like to thank Raisa Halonen from the Department of Media Technology in Helsinki University of Technology for providing the test material.  ... 
doi:10.1016/j.patrec.2011.04.006 fatcat:oidxps33uvbtlecd5frlkmqn7y

Multi-objective Hyper-parameter Optimization of Behavioral Song Embeddings [article]

Massimo Quadrana, Antoine Larreche-Mouly, Matthias Mauch
2022 arXiv   pre-print
We show that single-objective optimization can cause side effects on the non optimized metrics and propose a simple multi-objective optimization to mitigate these effects.  ...  We present new optimization objectives and metrics to monitor the effects of hyper-parameter optimization.  ...  We would like to thank Matt Jockers and all the members of the Music Machine Learning team for their help in improving and proofreading this work.  ... 
arXiv:2208.12724v1 fatcat:hd43fefmvjdtjnvpxctlpim56a
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