{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T04:22:22Z","timestamp":1750220542183,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":61,"publisher":"ACM","license":[{"start":{"date-parts":[[2021,8,14]],"date-time":"2021-08-14T00:00:00Z","timestamp":1628899200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2021,8,14]]},"DOI":"10.1145\/3447548.3467098","type":"proceedings-article","created":{"date-parts":[[2021,8,12]],"date-time":"2021-08-12T06:12:05Z","timestamp":1628748725000},"page":"3463-3471","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":4,"title":["Amazon SageMaker Automatic Model Tuning: Scalable Gradient-Free Optimization"],"prefix":"10.1145","author":[{"given":"Valerio","family":"Perrone","sequence":"first","affiliation":[{"name":"Amazon Web Services, Berlin, Germany"}]},{"given":"Huibin","family":"Shen","sequence":"additional","affiliation":[{"name":"Amazon Web Services, Berlin, Germany"}]},{"given":"Aida","family":"Zolic","sequence":"additional","affiliation":[{"name":"Amazon Web Services, Berlin, Germany"}]},{"given":"Iaroslav","family":"Shcherbatyi","sequence":"additional","affiliation":[{"name":"Amazon Web Services, Berlin, Germany"}]},{"given":"Amr","family":"Ahmed","sequence":"additional","affiliation":[{"name":"Amazon Web Services, Berlin, Germany"}]},{"given":"Tanya","family":"Bansal","sequence":"additional","affiliation":[{"name":"Amazon Web Services, Seattle, WA, USA"}]},{"given":"Michele","family":"Donini","sequence":"additional","affiliation":[{"name":"Amazon Web Services, Berlin, Germany"}]},{"given":"Fela","family":"Winkelmolen","sequence":"additional","affiliation":[{"name":"Amazon Web Services, Berlin, Germany"}]},{"given":"Rodolphe","family":"Jenatton","sequence":"additional","affiliation":[{"name":"Amazon Web Services, Berlin, Germany"}]},{"given":"Jean Baptiste","family":"Faddoul","sequence":"additional","affiliation":[{"name":"Amazon Web Services, Berlin, Germany"}]},{"given":"Barbara","family":"Pogorzelska","sequence":"additional","affiliation":[{"name":"Amazon Web Services, Berlin, Germany"}]},{"given":"Miroslav","family":"Miladinovic","sequence":"additional","affiliation":[{"name":"Amazon Web Services, Berlin, Germany"}]},{"given":"Krishnaram","family":"Kenthapadi","sequence":"additional","affiliation":[{"name":"Amazon Web Services, Palo Alto, CA, USA"}]},{"given":"Matthias","family":"Seeger","sequence":"additional","affiliation":[{"name":"Amazon Web Services, Berlin, Germany"}]},{"given":"C\u00e9dric","family":"Archambeau","sequence":"additional","affiliation":[{"name":"Amazon Web Services, Berlin, Germany"}]}],"member":"320","published-online":{"date-parts":[[2021,8,14]]},"reference":[{"key":"e_1_3_2_2_1_1","volume-title":"Heteroscedastic treed Bayesian optimisation. Technical report, preprint arXiv:1410.7172","author":"Assael J.-A. M.","year":"2014","unstructured":"J.-A. M. Assael , Z. Wang , and N. de Freitas . Heteroscedastic treed Bayesian optimisation. Technical report, preprint arXiv:1410.7172 , 2014 . J.-A. M. Assael, Z. Wang, and N. de Freitas. Heteroscedastic treed Bayesian optimisation. Technical report, preprint arXiv:1410.7172, 2014."},{"key":"e_1_3_2_2_2_1","volume-title":"Oct.","author":"Balandat M.","year":"2019","unstructured":"M. Balandat , B. Karrer , D. R. Jiang , S. Daulton , B. Letham , A. G. Wilson , and E. Bakshy . BoTorch: Programmable Bayesian Optimization in PyTorch. arXiv:1910.06403 , Oct. 2019 . M. Balandat, B. Karrer, D. R. Jiang, S. Daulton, B. Letham, A. G. Wilson, and E. Bakshy. BoTorch: Programmable Bayesian Optimization in PyTorch. arXiv:1910.06403, Oct. 2019."},{"key":"e_1_3_2_2_3_1","volume-title":"ICML","author":"Bardenet R.","year":"2013","unstructured":"R. Bardenet , M. Brendel , B. K\u00e9gl , and M. Sebag . Collaborative hyperparameter tuning . In ICML , 2013 . R. Bardenet, M. Brendel, B. K\u00e9gl, and M. Sebag. Collaborative hyperparameter tuning. In ICML, 2013."},{"key":"e_1_3_2_2_4_1","volume-title":"Random search for hyper-parameter optimization. Journal of Machine Learning Research (JMLR), 13","author":"Bergstra J.","year":"2012","unstructured":"J. Bergstra and Y. Bengio . Random search for hyper-parameter optimization. Journal of Machine Learning Research (JMLR), 13 , 2012 . J. Bergstra and Y. Bengio. Random search for hyper-parameter optimization. Journal of Machine Learning Research (JMLR), 13, 2012."},{"key":"e_1_3_2_2_5_1","volume-title":"A tutorial on Bayesian optimization of expensive cost functions, with application to active user modeling and hierarchical reinforcement learning. Technical report, preprint arXiv:1012.2599","author":"Brochu E.","year":"2010","unstructured":"E. Brochu , V. M. Cora , and N. De Freitas . A tutorial on Bayesian optimization of expensive cost functions, with application to active user modeling and hierarchical reinforcement learning. Technical report, preprint arXiv:1012.2599 , 2010 . E. Brochu, V. M. Cora, and N. De Freitas. A tutorial on Bayesian optimization of expensive cost functions, with application to active user modeling and hierarchical reinforcement learning. Technical report, preprint arXiv:1012.2599, 2010."},{"key":"e_1_3_2_2_6_1","doi-asserted-by":"publisher","DOI":"10.1145\/1015330.1015432"},{"key":"e_1_3_2_2_7_1","volume-title":"Amazon sagemaker autopilot: a white box automl solution at scale. arXiv:2012.08483","author":"Das P.","year":"2020","unstructured":"P. Das , V. Perrone , N. Ivkin , T. Bansal , Z. Karnin , H. Shen , I. Shcherbatyi , Y. Elor , W. Wu , A. Zolic , T. Lienart , A. Tang , A. Ahmed , J. B. Faddoul , R. Jenatton , F. Winkelmolen , P. Gautier , L. Dirac , A. Perunicic , M. Miladinovic , G. Zappella , C. Archambeau , M. Seeger , B. Dutt , and L. Rouesnel . Amazon sagemaker autopilot: a white box automl solution at scale. arXiv:2012.08483 , 2020 . P. Das, V. Perrone, N. Ivkin, T. Bansal, Z. Karnin, H. Shen, I. Shcherbatyi, Y. Elor, W. Wu, A. Zolic, T. Lienart, A. Tang, A. Ahmed, J. B. Faddoul, R. Jenatton, F. Winkelmolen, P. Gautier, L. Dirac, A. Perunicic, M. Miladinovic, G. Zappella, C. Archambeau, M. Seeger, B. Dutt, and L. Rouesnel. Amazon sagemaker autopilot: a white box automl solution at scale. arXiv:2012.08483, 2020."},{"key":"e_1_3_2_2_8_1","volume-title":"ICML AutoML Workshop","author":"Domhan T.","year":"2014","unstructured":"T. Domhan , T. Springenberg , and F. Hutter . Extrapolating learning curves of deep neural networks . In ICML AutoML Workshop , 2014 . T. Domhan, T. Springenberg, and F. Hutter. Extrapolating learning curves of deep neural networks. In ICML AutoML Workshop, 2014."},{"key":"e_1_3_2_2_9_1","first-page":"1436","volume-title":"ICML","author":"Falkner S.","year":"2018","unstructured":"S. Falkner , A. Klein , and F. Hutter . Bohb: Robust and efficient hyperparameter optimization at scale . In ICML , pages 1436 -- 1445 , 2018 . S. Falkner, A. Klein, and F. Hutter. Bohb: Robust and efficient hyperparameter optimization at scale. In ICML, pages 1436--1445, 2018."},{"key":"e_1_3_2_2_10_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-05318-5_1"},{"key":"e_1_3_2_2_11_1","doi-asserted-by":"publisher","DOI":"10.5555\/2887007.2887164"},{"key":"e_1_3_2_2_12_1","volume-title":"NeurIPS","author":"Gardner J. R.","year":"2018","unstructured":"J. R. Gardner , G. Pleiss , D. Bindel , K. Q. Weinberger , and A. G. Wilson . GPyTorch: Blackbox matrix-matrix Gaussian process inference with GPU acceleration . In NeurIPS , 2018 . J. R. Gardner, G. Pleiss, D. Bindel, K. Q. Weinberger, and A. G. Wilson. GPyTorch: Blackbox matrix-matrix Gaussian process inference with GPU acceleration. In NeurIPS, 2018."},{"key":"e_1_3_2_2_13_1","doi-asserted-by":"publisher","DOI":"10.5555\/3020751.3020778"},{"key":"e_1_3_2_2_14_1","doi-asserted-by":"publisher","DOI":"10.1145\/3097983.3098043"},{"key":"e_1_3_2_2_15_1","volume-title":"Pareto-efficient Acquisition Functions for Cost-Aware Bayesian Optimization. NeurIPS Meta Learning Workshop","author":"Guinet G.","year":"2020","unstructured":"G. Guinet , V. Perrone , and C. Archambeau . Pareto-efficient Acquisition Functions for Cost-Aware Bayesian Optimization. NeurIPS Meta Learning Workshop , 2020 . G. Guinet, V. Perrone, and C. Archambeau. Pareto-efficient Acquisition Functions for Cost-Aware Bayesian Optimization. NeurIPS Meta Learning Workshop, 2020."},{"key":"e_1_3_2_2_16_1","volume-title":"Completely derandomized self-adaptation in evolution strategies. Evolutionary Computation, 9(2)","author":"Hansen N.","year":"2001","unstructured":"N. Hansen and A. Ostermeier . Completely derandomized self-adaptation in evolution strategies. Evolutionary Computation, 9(2) , 2001 . N. Hansen and A. Ostermeier. Completely derandomized self-adaptation in evolution strategies. Evolutionary Computation, 9(2), 2001."},{"key":"e_1_3_2_2_17_1","volume-title":"Journal of Machine Learning Research (JMLR)","author":"Hennig P.","year":"2012","unstructured":"P. Hennig and C. J. Schuler . Entropy search for information-efficient global optimization . Journal of Machine Learning Research (JMLR) , 2012 . P. Hennig and C. J. Schuler. Entropy search for information-efficient global optimization. Journal of Machine Learning Research (JMLR), 2012."},{"key":"e_1_3_2_2_18_1","volume-title":"Predictive entropy search for efficient global optimization of black-box functions. Technical report, preprint arXiv:1406.2541","author":"Hern\u00e1ndez-Lobato J. M.","year":"2014","unstructured":"J. M. Hern\u00e1ndez-Lobato , M. W. Hoffman , and Z. Ghahramani . Predictive entropy search for efficient global optimization of black-box functions. Technical report, preprint arXiv:1406.2541 , 2014 . J. M. Hern\u00e1ndez-Lobato, M. W. Hoffman, and Z. Ghahramani. Predictive entropy search for efficient global optimization of black-box functions. Technical report, preprint arXiv:1406.2541, 2014."},{"key":"e_1_3_2_2_19_1","first-page":"365","volume-title":"Proceedings of the International Conference on Artificial Intelligence and Statistics (AISTATS)","author":"Hoffman M.","year":"2014","unstructured":"M. Hoffman , B. Shahriari , and N. de Freitas . On correlation and budget constraints in model-based bandit optimization with application to automatic machine learning . In Proceedings of the International Conference on Artificial Intelligence and Statistics (AISTATS) , pages 365 -- 374 , 2014 . M. Hoffman, B. Shahriari, and N. de Freitas. On correlation and budget constraints in model-based bandit optimization with application to automatic machine learning. In Proceedings of the International Conference on Artificial Intelligence and Statistics (AISTATS), pages 365--374, 2014."},{"key":"e_1_3_2_2_20_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-25566-3_40"},{"key":"e_1_3_2_2_21_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-25566-3_40"},{"key":"e_1_3_2_2_22_1","volume-title":"Population based training of neural networks. Technical report, preprint arXiv:1711.09846","author":"Jaderberg M.","year":"2017","unstructured":"M. Jaderberg , V. Dalibard , S. Osindero , W. M. Czarnecki , J. Donahue , A. Razavi , O. Vinyals , T. Green , I. Dunning , K. Simonyan , Population based training of neural networks. Technical report, preprint arXiv:1711.09846 , 2017 . M. Jaderberg, V. Dalibard, S. Osindero, W. M. Czarnecki, J. Donahue, A. Razavi, O. Vinyals, T. Green, I. Dunning, K. Simonyan, et al. Population based training of neural networks. Technical report, preprint arXiv:1711.09846, 2017."},{"key":"e_1_3_2_2_23_1","volume-title":"Non-stochastic best arm identification and hyperparameter optimization. Technical report, preprint arXiv:1502.07943","author":"Jamieson K.","year":"2015","unstructured":"K. Jamieson and A. Talwalkar . Non-stochastic best arm identification and hyperparameter optimization. Technical report, preprint arXiv:1502.07943 , 2015 . K. Jamieson and A. Talwalkar. Non-stochastic best arm identification and hyperparameter optimization. Technical report, preprint arXiv:1502.07943, 2015."},{"key":"e_1_3_2_2_24_1","volume-title":"Journal of Global Optimization","author":"Jones D. R.","year":"1998","unstructured":"D. R. Jones , M. Schonlau , and W. J. Welch . Efficient global optimization of expensive black-box functions . Journal of Global Optimization , 1998 . D. R. Jones, M. Schonlau, and W. J. Welch. Efficient global optimization of expensive black-box functions. Journal of Global Optimization, 1998."},{"key":"e_1_3_2_2_25_1","volume-title":"ICML","author":"Karnin Z.","year":"2013","unstructured":"Z. Karnin , T. Koren , and O. Somekh . Almost optimal exploration in multi-armed bandits . In ICML , 2013 . Z. Karnin, T. Koren, and O. Somekh. Almost optimal exploration in multi-armed bandits. In ICML, 2013."},{"key":"e_1_3_2_2_26_1","volume-title":"International Conference on Learning Representations (ICLR)","volume":"17","author":"Klein A.","year":"2017","unstructured":"A. Klein , S. Falkner , J. T. Springenberg , and F. Hutter . Learning curve prediction with Bayesian neural networks . In International Conference on Learning Representations (ICLR) , volume 17 , 2017 . A. Klein, S. Falkner, J. T. Springenberg, and F. Hutter. Learning curve prediction with Bayesian neural networks. In International Conference on Learning Representations (ICLR), volume 17, 2017."},{"key":"e_1_3_2_2_27_1","volume-title":"Model-based asynchronous hyperparameter and neural architecture search. arXiv preprint arXiv:2003.10865","author":"Klein A.","year":"2020","unstructured":"A. Klein , L. Tiao , T. Lienart , C. Archambeau , and M. Seeger . Model-based asynchronous hyperparameter and neural architecture search. arXiv preprint arXiv:2003.10865 , 2020 . A. Klein, L. Tiao, T. Lienart, C. Archambeau, and M. Seeger. Model-based asynchronous hyperparameter and neural architecture search. arXiv preprint arXiv:2003.10865, 2020."},{"key":"e_1_3_2_2_28_1","volume-title":"GPflowOpt: A Bayesian Optimization Library using TensorFlow. arXiv preprint -- arXiv:1711.03845","author":"Knudde N.","year":"2017","unstructured":"N. Knudde , J. van der Herten , T. Dhaene , and I. Couckuyt . GPflowOpt: A Bayesian Optimization Library using TensorFlow. arXiv preprint -- arXiv:1711.03845 , 2017 . N. Knudde, J. van der Herten, T. Dhaene, and I. Couckuyt. GPflowOpt: A Bayesian Optimization Library using TensorFlow. arXiv preprint -- arXiv:1711.03845, 2017."},{"key":"e_1_3_2_2_29_1","volume-title":"ICML AutoML Workshop","author":"Lee E. H.","year":"2020","unstructured":"E. H. Lee , V. Perrone , C. Archambeau , and M. Seeger . Cost-aware Bayesian optimization . In ICML AutoML Workshop , 2020 . E. H. Lee, V. Perrone, C. Archambeau, and M. Seeger. Cost-aware Bayesian optimization. In ICML AutoML Workshop, 2020."},{"key":"e_1_3_2_2_30_1","volume-title":"Hyperband: A novel bandit-based approach to hyperparameter optimization. Technical report, preprint arXiv:1603.06560","author":"Li L.","year":"2016","unstructured":"L. Li , K. Jamieson , G. DeSalvo , A. Rostamizadeh , and A. Talwalkar . Hyperband: A novel bandit-based approach to hyperparameter optimization. Technical report, preprint arXiv:1603.06560 , 2016 . L. Li, K. Jamieson, G. DeSalvo, A. Rostamizadeh, and A. Talwalkar. Hyperband: A novel bandit-based approach to hyperparameter optimization. Technical report, preprint arXiv:1603.06560, 2016."},{"key":"e_1_3_2_2_32_1","doi-asserted-by":"publisher","DOI":"10.1145\/3318464.3386126"},{"key":"e_1_3_2_2_33_1","unstructured":"D. J. Lizotte. Practical Bayesian optimization. PhD thesis University of Alberta 2008.  D. J. Lizotte. Practical Bayesian optimization. PhD thesis University of Alberta 2008."},{"key":"e_1_3_2_2_34_1","volume-title":"Inference and Learning Algorithms","author":"Mackay D. J. C.","year":"2003","unstructured":"D. J. C. Mackay . Information Theory , Inference and Learning Algorithms . Cambridge University Press , 2003 . D. J. C. Mackay. Information Theory, Inference and Learning Algorithms. Cambridge University Press, 2003."},{"key":"e_1_3_2_2_35_1","unstructured":"A. G. d. G. Matthews M. van der Wilk T. Nickson K. Fujii A. Boukouvalas P. Le\u00f3n-Villagr\u00e1 Z. Ghahramani and J. Hensman. GPflow: A Gaussian process library using TensorFlow. Journal of Machine Learning Research 18(40) apr 2017.  A. G. d. G. Matthews M. van der Wilk T. Nickson K. Fujii A. Boukouvalas P. Le\u00f3n-Villagr\u00e1 Z. Ghahramani and J. Hensman. GPflow: A Gaussian process library using TensorFlow. Journal of Machine Learning Research 18(40) apr 2017."},{"key":"e_1_3_2_2_36_1","volume-title":"Towards Global Optimization","author":"Mockus J.","year":"1978","unstructured":"J. Mockus , V. Tiesis , and A. Zilinskas . The application of Bayesian methods for seeking the extremum . Towards Global Optimization , 1978 . J. Mockus, V. Tiesis, and A. Zilinskas. The application of Bayesian methods for seeking the extremum. Towards Global Optimization, 1978."},{"key":"e_1_3_2_2_37_1","first-page":"1732","volume-title":"NeurIPS","author":"Murray I.","year":"2010","unstructured":"I. Murray and R. P. Adams . Slice sampling covariance hyperparameters of latent Gaussian models . In NeurIPS , pages 1732 -- 1740 , 2010 . I. Murray and R. P. Adams. Slice sampling covariance hyperparameters of latent Gaussian models. In NeurIPS, pages 1732--1740, 2010."},{"key":"e_1_3_2_2_38_1","volume-title":"Bayesian optimization: Open source constrained global optimization tool for Python","author":"Nogueira F.","year":"2014","unstructured":"F. Nogueira . Bayesian optimization: Open source constrained global optimization tool for Python , 2014 . F. Nogueira. Bayesian optimization: Open source constrained global optimization tool for Python, 2014."},{"key":"e_1_3_2_2_39_1","volume-title":"Proceedings of the 3rd Learning and Intelligent OptimizatioN Conference (LION 3)","author":"Osborne M. A.","year":"2009","unstructured":"M. A. Osborne , R. Garnett , and S. J. Roberts . Gaussian processes for global optimization . In Proceedings of the 3rd Learning and Intelligent OptimizatioN Conference (LION 3) , 2009 . M. A. Osborne, R. Garnett, and S. J. Roberts. Gaussian processes for global optimization. In Proceedings of the 3rd Learning and Intelligent OptimizatioN Conference (LION 3), 2009."},{"key":"e_1_3_2_2_40_1","volume-title":"Second Workshop on Machine Learning and the Physical Sciences, NeurIPS","author":"Paleyes A.","year":"2019","unstructured":"A. Paleyes , M. Pullin , M. Mahsereci , N. Lawrence , and J. Gonz\u00e1lez . Emulation of physical processes with Emukit . In Second Workshop on Machine Learning and the Physical Sciences, NeurIPS , 2019 . A. Paleyes, M. Pullin, M. Mahsereci, N. Lawrence, and J. Gonz\u00e1lez. Emulation of physical processes with Emukit. In Second Workshop on Machine Learning and the Physical Sciences, NeurIPS, 2019."},{"key":"e_1_3_2_2_41_1","volume-title":"NIPS-W","author":"Paszke A.","year":"2017","unstructured":"A. Paszke , S. Gross , S. Chintala , G. Chanan , E. Yang , Z. DeVito , Z. Lin , A. Desmaison , L. Antiga , and A. Lerer . Automatic differentiation in PyTorch . In NIPS-W , 2017 . A. Paszke, S. Gross, S. Chintala, G. Chanan, E. Yang, Z. DeVito, Z. Lin, A. Desmaison, L. Antiga, and A. Lerer. Automatic differentiation in PyTorch. In NIPS-W, 2017."},{"key":"e_1_3_2_2_42_1","volume-title":"ICML AutoML Workshop","author":"Perrone V.","year":"2020","unstructured":"V. Perrone , M. Donini , K. Kenthapadi , and C. Archambeau . Fair Bayesian optimization . In ICML AutoML Workshop , 2020 . V. Perrone, M. Donini, K. Kenthapadi, and C. Archambeau. Fair Bayesian optimization. In ICML AutoML Workshop, 2020."},{"key":"e_1_3_2_2_43_1","volume-title":"NeurIPS Meta Learning Workshop","author":"Perrone V.","year":"2017","unstructured":"V. Perrone , R. Jenatton , M. Seeger , and C. Archambeau . Multiple adaptive Bayesian linear regression for scalable Bayesian optimization with warm start . NeurIPS Meta Learning Workshop , 2017 . V. Perrone, R. Jenatton, M. Seeger, and C. Archambeau. Multiple adaptive Bayesian linear regression for scalable Bayesian optimization with warm start. NeurIPS Meta Learning Workshop, 2017."},{"key":"e_1_3_2_2_44_1","volume-title":"NeurIPS","author":"Perrone V.","year":"2018","unstructured":"V. Perrone , R. Jenatton , M. W. Seeger , and C. Archambeau . Scalable hyperparameter transfer learning . NeurIPS , 2018 . V. Perrone, R. Jenatton, M. W. Seeger, and C. Archambeau. Scalable hyperparameter transfer learning. NeurIPS, 2018."},{"key":"e_1_3_2_2_45_1","volume-title":"NeurIPS Meta Learning Workshop","author":"Perrone V.","year":"2019","unstructured":"V. Perrone , I. Shcherbatyi , R. Jenatton , C. Archambeau , and M. Seeger . Constrained Bayesian optimization with max-value entropy search . In NeurIPS Meta Learning Workshop , 2019 . V. Perrone, I. Shcherbatyi, R. Jenatton, C. Archambeau, and M. Seeger. Constrained Bayesian optimization with max-value entropy search. In NeurIPS Meta Learning Workshop, 2019."},{"key":"e_1_3_2_2_46_1","first-page":"32","article-title":"Learning search spaces for Bayesian optimization: Another view of hyperparameter transfer learning","author":"Perrone V.","year":"2019","unstructured":"V. Perrone , H. Shen , M. W. Seeger , C. Archambeau , and R. Jenatton . Learning search spaces for Bayesian optimization: Another view of hyperparameter transfer learning . NeurIPS , 32 , 2019 . V. Perrone, H. Shen, M. W. Seeger, C. Archambeau, and R. Jenatton. Learning search spaces for Bayesian optimization: Another view of hyperparameter transfer learning. NeurIPS, 32, 2019.","journal-title":"NeurIPS"},{"key":"e_1_3_2_2_47_1","volume-title":"Gaussian Processes for Machine Learning","author":"Rasmussen C.","year":"2006","unstructured":"C. Rasmussen and C. Williams . Gaussian Processes for Machine Learning . MIT Press , 2006 . C. Rasmussen and C. Williams. Gaussian Processes for Machine Learning. MIT Press, 2006."},{"key":"e_1_3_2_2_48_1","volume-title":"ICML","author":"Real E.","year":"2020","unstructured":"E. Real , C. Liang , D. So , and Q. Le . Evolving machine learning algorithms from scratch . In ICML , 2020 . E. Real, C. Liang, D. So, and Q. Le. Evolving machine learning algorithms from scratch. In ICML, 2020."},{"key":"e_1_3_2_2_49_1","volume-title":"ICML","author":"Salinas D.","year":"2020","unstructured":"D. Salinas , H. Shen , and V. Perrone . A quantile-based approach for hyperparameter transfer learning . ICML , 2020 . D. Salinas, H. Shen, and V. Perrone. A quantile-based approach for hyperparameter transfer learning. ICML, 2020."},{"key":"e_1_3_2_2_50_1","volume-title":"Taking the human out of the loop: A review of Bayesian optimization","author":"Shahriari B.","year":"2016","unstructured":"B. Shahriari , K. Swersky , Z. Wang , R. P. Adams , and N. de Freitas . Taking the human out of the loop: A review of Bayesian optimization . IEEE , 2016 . B. Shahriari, K. Swersky, Z. Wang, R. P. Adams, and N. de Freitas. Taking the human out of the loop: A review of Bayesian optimization. IEEE, 2016."},{"key":"e_1_3_2_2_51_1","first-page":"2960","volume-title":"NeurIPS","author":"Snoek J.","year":"2012","unstructured":"J. Snoek , H. Larochelle , and R. P. Adams . Practical Bayesian optimization of machine learning algorithms . In NeurIPS , pages 2960 -- 2968 , 2012 . J. Snoek, H. Larochelle, and R. P. Adams. Practical Bayesian optimization of machine learning algorithms. In NeurIPS, pages 2960--2968, 2012."},{"key":"e_1_3_2_2_52_1","volume-title":"Input warping for Bayesian optimization of non-stationary functions. Technical report, preprint arXiv:1402.0929","author":"Snoek J.","year":"2014","unstructured":"J. Snoek , K. Swersky , R. S. Zemel , and R. P. Adams . Input warping for Bayesian optimization of non-stationary functions. Technical report, preprint arXiv:1402.0929 , 2014 . J. Snoek, K. Swersky, R. S. Zemel, and R. P. Adams. Input warping for Bayesian optimization of non-stationary functions. Technical report, preprint arXiv:1402.0929, 2014."},{"key":"e_1_3_2_2_53_1","volume-title":"USSR Computational Mathematics and Mathematical Physics, 7(4)","author":"Sobol I. M.","year":"1967","unstructured":"I. M. Sobol . On the distribution of points in a cube and the approximate evaluation of integrals. USSR Computational Mathematics and Mathematical Physics, 7(4) , 1967 . I. M. Sobol. On the distribution of points in a cube and the approximate evaluation of integrals. USSR Computational Mathematics and Mathematical Physics, 7(4), 1967."},{"key":"e_1_3_2_2_54_1","first-page":"4134","volume-title":"NeurIPS","author":"Springenberg J. T.","year":"2016","unstructured":"J. T. Springenberg , A. Klein , S. Falkner , and F. Hutter . Bayesian optimization with robust Bayesian neural networks . In NeurIPS , pages 4134 -- 4142 , 2016 . J. T. Springenberg, A. Klein, S. Falkner, and F. Hutter. Bayesian optimization with robust Bayesian neural networks. In NeurIPS, pages 4134--4142, 2016."},{"key":"e_1_3_2_2_55_1","volume-title":"Gaussian process optimization in the bandit setting: No regret and experimental design. ICML, page 1015--1022","author":"Srinivas N.","year":"2010","unstructured":"N. Srinivas , A. Krause , S. Kakade , and M. Seeger . Gaussian process optimization in the bandit setting: No regret and experimental design. ICML, page 1015--1022 , 2010 . N. Srinivas, A. Krause, S. Kakade, and M. Seeger. Gaussian process optimization in the bandit setting: No regret and experimental design. ICML, page 1015--1022, 2010."},{"key":"e_1_3_2_2_56_1","first-page":"58","article-title":"Information-theoretic regret bounds for Gaussian process optimization in the bandit setting","author":"Srinivas N.","year":"2012","unstructured":"N. Srinivas , A. Krause , S. Kakade , and M. Seeger . Information-theoretic regret bounds for Gaussian process optimization in the bandit setting . IEEE Transactions on Information Theory , 58 , 2012 . N. Srinivas, A. Krause, S. Kakade, and M. Seeger. Information-theoretic regret bounds for Gaussian process optimization in the bandit setting. IEEE Transactions on Information Theory, 58, 2012.","journal-title":"IEEE Transactions on Information Theory"},{"key":"e_1_3_2_2_57_1","first-page":"2004","volume-title":"NeurIPS","author":"Swersky K.","year":"2013","unstructured":"K. Swersky , J. Snoek , and R. P. Adams . Multi-task Bayesian optimization . In NeurIPS , pages 2004 -- 2012 , 2013 . K. Swersky, J. Snoek, and R. P. Adams. Multi-task Bayesian optimization. In NeurIPS, pages 2004--2012, 2013."},{"key":"e_1_3_2_2_58_1","volume-title":"Freeze-thaw Bayesian optimization. Technical report, preprint arXiv:1406.3896","author":"Swersky K.","year":"2014","unstructured":"K. Swersky , J. Snoek , and R. P. Adams . Freeze-thaw Bayesian optimization. Technical report, preprint arXiv:1406.3896 , 2014 . K. Swersky, J. Snoek, and R. P. Adams. Freeze-thaw Bayesian optimization. Technical report, preprint arXiv:1406.3896, 2014."},{"key":"e_1_3_2_2_59_1","first-page":"285","volume-title":"Biometrika","author":"Thompson W. R.","year":"1933","unstructured":"W. R. Thompson . On the likelihood that one unknown probability exceeds another in view of the evidence of two samples . Biometrika , pages 285 -- 294 , 1933 . W. R. Thompson. On the likelihood that one unknown probability exceeds another in view of the evidence of two samples. Biometrika, pages 285--294, 1933."},{"key":"e_1_3_2_2_60_1","volume-title":"Springer science & business media","author":"Vapnik V.","year":"2013","unstructured":"V. Vapnik . The nature of statistical learning theory. Springer science & business media , 2013 . V. Vapnik. The nature of statistical learning theory. Springer science & business media, 2013."},{"key":"e_1_3_2_2_61_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10898-008-9354-2"},{"key":"e_1_3_2_2_62_1","first-page":"70","article-title":"Max-value entropy search for efficient Bayesian optimization","author":"Wang Z.","year":"2017","unstructured":"Z. Wang and S. Jegelka . Max-value entropy search for efficient Bayesian optimization . ICML , 70 , 2017 . Z. Wang and S. Jegelka. Max-value entropy search for efficient Bayesian optimization. ICML, 70, 2017.","journal-title":"ICML"}],"event":{"name":"KDD '21: The 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","sponsor":["SIGMOD ACM Special Interest Group on Management of Data","SIGKDD ACM Special Interest Group on Knowledge Discovery in Data"],"location":"Virtual Event Singapore","acronym":"KDD '21"},"container-title":["Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery &amp; Data Mining"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3447548.3467098","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3447548.3467098","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T21:28:05Z","timestamp":1750195685000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3447548.3467098"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,8,14]]},"references-count":61,"alternative-id":["10.1145\/3447548.3467098","10.1145\/3447548"],"URL":"https:\/\/doi.org\/10.1145\/3447548.3467098","relation":{},"subject":[],"published":{"date-parts":[[2021,8,14]]},"assertion":[{"value":"2021-08-14","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}