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Hyperparameter optimization is crucial to achieving high performance in deep learning.
On finding the maxima of a set of vectors
Kung, H., Luccio, F., and Preparata, F. (1975) · 1975
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Multidimensional scaling
Kruskal, J. and Wish, M. (1978) · 1978
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Review of the development of multidimensional scaling methods
Mead, A. (1992) · 1992
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A fast and elitist multiobjective genetic algorithm: NSGA-II
Deb, K., Pratap, A., Agarwal, S., and Meyarivan, T. (2002) · 2002
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A summary-attainment-surface plotting method for visualizing the performance of stochastic multiobjective optimizers
Knowles, J. (2005) · 2005
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On the computation of the empirical attainment function
Fonseca, C., Guerreiro, A., López-Ibánez, M., and Paquete, L. (2011) · 2011
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An approach to visualizing the 3D empirical attainment function
Tušar, T. and Filipič, B. (2013) · 2013
Cited alongside, same era.
Bayesian optimization in AlphaGo
Chen, Y., Huang, A., Wang, Z., Antonoglou, I., Schrittwieser, J., Silver, D., and de Freitas, N. (2018) · 2018
Cited alongside, same era.
Deep reinforcement learning that matters
Henderson, P., Islam, R., Bachman, P., Pineau, J., Precup, D., and Meger, D. (2018) · 2018
Cited alongside, same era.
Multiobjective tree-structured Parzen estimator for computationally expensive optimization problems
Ozaki, Y., Tanigaki, Y., Watanabe, S., and Onishi, M. (2020) · 2020
Cited alongside, same era.
Multiobjective tree-structured Parzen estimator
Ozaki, Y., Tanigaki, Y., Watanabe, S., Nomura, M., and Onishi, M. (2022) · 2022
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Multi-objective tree-structured Parzen estimator meets meta-learning
Watanabe, S., Awad, N., Onishi, M., and Hutter, F. (2022) · 2022
Later among the works it cites.
MO-DEHB: Evolutionary-based Hyperband for multi-objective optimization
Awad, N., Sharma, A., and Hutter, F. (2023) · 2023
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Watanabe, S., Awad, N., Onishi, M., and Hutter, F. (2023) · 2023
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