Fetching the paper…
Reading the bibliography…
We present mlrMBO, a flexible and comprehensive R toolbox for model-based optimization (MBO), also known as Bayesian optimization, which addresses the problem of expensive black-box optimization by approximating the given objective function through a surrogate regression model.
Design and analysis of computer experiments
Sacks, J., Welch, W.J., Mitchell, T.J., Wynn, H.P. · 1989
Earlier work this paper cites.
Efficient global optimization of expensive black-box functions
Jones, D.R., Schonlau, M., Welch, W.J. · 1998
Earlier work this paper cites.
A comparison of three methods for selecting values of input variables in the analysis of output from a computer code
McKay, M.D., Beckman, R.J., Conover, W.J. · 2000
Earlier work this paper cites.
A taxonomy of global optimization methods based on response surfaces
Jones, D.R. · 2001
Earlier work this paper cites.
A fast and elitist multiobjective genetic algorithm: NSGA-II
Deb, K., Pratap, A., Agarwal, S., Meyarivan, T. · 2002
Earlier work this paper cites.
Gaussian Processes for Machine Learning
Rasmussen, C.E., Williams, C.K.I. · 2006
Earlier work this paper cites.
Considerations of budget allocation for sequential parameter optimization (spo
Preuss, M. · 2006
Earlier work this paper cites.
ParEGO: A hybrid algorithm with on-line landscape approximation for expensive multiobjective optimization problems
Knowles, J. · 2006
Earlier work this paper cites.
Multiobjective optimization on a limited amount of evaluations using model-assisted 𝒮 \mathcal{S} -metric selection
Ponweiser, W., Wagner, T., Biermann, D., Vincze, M. · 2008
Earlier work this paper cites.
Standard errors for bagged and random forest estimators
Sexton, J., Laake, P. · 2009
Earlier work this paper cites.
Empirical modeling of hard turning of aisi 6150 steel using design and analysis of computer experiments
Sieben, B., Wagner, T., Biermann, D. · 2010
Earlier work this paper cites.
Kriging is well-suited to parallelize optimization
Ginsbourger, D., Le Riche, R., Carraro, L. · 2010
Earlier work this paper cites.
Convergence properties of the expected improvement algorithm with fixed mean and covariance functions
Vazquez, E., Bect, J. · 2010
Earlier work this paper cites.
An investigation of missing data methods for classification trees applied to binary response data
Ding, Y., Simonoff, J.S. · 2010
Earlier work this paper cites.
Sequential model-based optimization for general algorithm configuration
Hutter, F., Hoos, H.H., Leyton-Brown, K. · 2011
Earlier work this paper cites.
Algorithms for hyper-parameter optimization
Bergstra, J.S., Bardenet, R., Bengio, Y., Kégl, B. · 2011
Cited alongside, same era.
DiceKriging, DiceOptim: Two R packages for the analysis of computer experiments by kriging-based metamodeling and optimization
Roustant, O., Ginsbourger, D., Deville, Y. · 2012
Cited alongside, same era.
Practical bayesian optimization of machine learning algorithms
Snoek, J., Larochelle, H., Adams, R.P. · 2012
Cited alongside, same era.
A gentle introduction to sequential parameter optimization
Bartz-Beielstein, T., Zaefferer, M. · 2012
Cited alongside, same era.
Tuning and evolution of support vector kernels
Koch, P., Bischl, B., Flasch, O., Bartz-Beielstein, T., Weihs, C., Konen, W. · 2012
Cited alongside, same era.
A simple approach to emulation for computer models with qualitative and quantitative factors
Zhou, Q., Qian, P.Z., Zhou, S. · 2012
Confidence intervals for random forests: The jackknife and the infinitesimal jackknife
Wager, S., Hastie, T., Efron, B. · 2014
Later among the works it cites.
Model-based multi-objective optimization: Taxonomy, multi-point proposal, toolbox and benchmark
Horn, D., Wagner, T., Biermann, D., Weihs, C., Bischl, B. · 2015
Later among the works it cites.
Automatic model selection for high-dimensional survival analysis
Lang, M., Kotthaus, H., Marwedel, P., Weihs, C., Rahnenführer, J., Bischl, B. · 2015
Later among the works it cites.
BatchJobs and BatchExperiments: Abstraction mechanisms for using R in batch environments
Bischl, B., Lang, M., Mersmann, O., Rahnenführer, J., Weihs, C. · 2015
Later among the works it cites.
Multi-objective parameter configuration of machine learning algorithms using model-based optimization
Horn, D., Bischl, B. · 2016
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Parallel algorithm configuration
Hutter, F., Hoos, H.H., Leyton-Brown, K. · 2012
Cited alongside, same era.
Auto-WEKA: Combined selection and hyperparameter optimization of classification algorithms
Thornton, C., Hutter, F., Hoos, H.H., Leyton-Brown, K. · 2013
Cited alongside, same era.
PROGRESS: Progressive reinforcement-learning-based surrogate selection
Hess, S., Wagner, T., Bischl, B. · 2013
Cited alongside, same era.
A case study on multi-criteria optimization of an event detection software under limited budgets
Zaefferer, M., Bartz-Beielstein, T., Naujoks, B., Wagner, T., Emmerich, M. · 2013
Cited alongside, same era.
Planning and Multi-Objective Optimization of Manufacturing Processes by Means of Empirical Surrogate Models
Wagner, T. · 2013
Cited alongside, same era.
Towards an empirical foundation for assessing bayesian optimization of hyperparameters
Eggensperger, K., Feurer, M., Hutter, F., Bergstra, J., Snoek, J., Hoos, H., Leyton-Brown, K. · 2013
Cited alongside, same era.
rBayesianOptimization: Bayesian Optimization of Hyperparameters; 2016
Yan, Y. · 2016
Later among the works it cites.
Predictive entropy search for multi-objective bayesian optimization
Hernández-Lobato, D., Hernández-Lobato, J.M., Shah, A., Adams, R.P. · 2016
Later among the works it cites.
mlr: Machine learning in R
Bischl, B., Lang, M., Kotthoff, L., Schiffner, J., Richter, J., Studerus, E., Casalicchio, G., Jones, Z.M. · 2016
Later among the works it cites.
A comparative study on large scale kernelized support vector machines
Horn, D., Demircioğlu, A., Bischl, B., Glasmachers, T., Weihs, C. · 2016
Later among the works it cites.
On Sampling Methods for Costly Multi-Objective Black-Box Optimization
Steponavič, I., Shirazi-Manesh, M., Hyndman, R.J., Smith-Miles, K., Villanova, L. · 2016
Later among the works it cites.
Schiffner, J., Bischl, B., Lang, M., Richter, J., Jones, Z.M., Probst, P., Pfisterer, F., Gallo, M., Kirchhoff, D., Kühn, T., Thomas, J., Kotthoff, L. · 2016
Later among the works it cites.
cmaesr: Covariance Matrix Adaptation Evolution Strategy; 2016
Bossek, J. · 2016
Later among the works it cites.
COCO: the bi-objective black box optimization benchmarking (bbob-biobj) test suite
Tusar, T., Brockhoff, D., Hansen, N., Auger, A. · 2016
Later among the works it cites.
smoof: Single- and Multi-Objective Optimization Test Functions
Bossek, J. · 2017
Closest in time.