{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,10]],"date-time":"2026-03-10T18:43:35Z","timestamp":1773168215992,"version":"3.50.1"},"reference-count":34,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2023,1,16]],"date-time":"2023-01-16T00:00:00Z","timestamp":1673827200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,16]],"date-time":"2023-01-16T00:00:00Z","timestamp":1673827200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Sci Comput"],"published-print":{"date-parts":[[2023,3]]},"DOI":"10.1007\/s10915-022-02083-4","type":"journal-article","created":{"date-parts":[[2023,1,16]],"date-time":"2023-01-16T07:02:51Z","timestamp":1673852571000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":24,"title":["Goal-Oriented Sensitivity Analysis of Hyperparameters in Deep Learning"],"prefix":"10.1007","volume":"94","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1053-8694","authenticated-orcid":false,"given":"Paul","family":"Novello","sequence":"first","affiliation":[]},{"given":"Ga\u00ebl","family":"Po\u00ebtte","sequence":"additional","affiliation":[]},{"given":"David","family":"Lugato","sequence":"additional","affiliation":[]},{"given":"Pietro Marco","family":"Congedo","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,1,16]]},"reference":[{"key":"2083_CR1","unstructured":"Goodfellow, I., Bengio, Y., Courville, A.: Deep Learning (2016). http:\/\/www.deeplearningbook.org"},{"key":"2083_CR2","doi-asserted-by":"publisher","unstructured":"Gretton, A., Bousquet, O., Smola, A., Sch\u00f6lkopf, B.: Measuring statistical dependence with Hilbert\u2013Schmidt norms. In: Proceedings of the 16th International Conference on Algorithmic Learning Theory. ALT\u201905, pp. 63\u201377. Springer, Berlin, Heidelberg (2005). https:\/\/doi.org\/10.1007\/11564089_7","DOI":"10.1007\/11564089_7"},{"key":"2083_CR3","unstructured":"Gretton, A., Borgwardt, K., Rasch, M., Sch\u00f6lkopf, B., Smola, A.J.: A kernel method for the two-sample-problem. In: Sch\u00f6lkopf, B., Platt, J.C., Hoffman, T. (eds.) Advances in Neural Information Processing Systems 19 (2007). http:\/\/papers.nips.cc\/paper\/3110-a-kernel-method-for-the-two-sample-problem.pdf"},{"key":"2083_CR4","unstructured":"Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 448\u2013456. PMLR, Lille, France (2015). https:\/\/proceedings.mlr.press\/v37\/ioffe15.html"},{"key":"2083_CR5","unstructured":"Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: ICLR (Poster) (2015). arXiv:1412.6980"},{"key":"2083_CR6","unstructured":"Tan, M., Le, Q.: EfficientNet: rethinking model scaling for convolutional neural networks. In: Chaudhuri, K., Salakhutdinov, R. (eds.) Proceedings of the 36th International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 97, pp. 6105\u20136114. PMLR, Long Beach, California, USA (2019). http:\/\/proceedings.mlr.press\/v97\/tan19a.html"},{"issue":"10","key":"2083_CR7","first-page":"281","volume":"13","author":"J Bergstra","year":"2012","unstructured":"Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. J. Mach. Learn. Res. 13(10), 281\u2013305 (2012)","journal-title":"J. Mach. Learn. Res."},{"key":"2083_CR8","unstructured":"Jamieson, K., Talwalkar, A.: Non-stochastic best arm identification and hyperparameter optimization. In: Gretton, A., Robert, C.C. (eds.) Proceedings of the 19th International Conference on Artificial Intelligence and Statistics. Proceedings of Machine Learning Research, vol. 51, pp. 240\u2013248. PMLR, Cadiz, Spain (2016). http:\/\/proceedings.mlr.press\/v51\/jamieson16.html"},{"issue":"185","key":"2083_CR9","first-page":"1","volume":"18","author":"L Li","year":"2018","unstructured":"Li, L., Jamieson, K., DeSalvo, G., Rostamizadeh, A., Talwalkar, A.: Hyperband: a novel bandit-based approach to hyperparameter optimization. J. Mach. Learn. Res. 18(185), 1\u201352 (2018)","journal-title":"J. Mach. Learn. Res."},{"key":"2083_CR10","doi-asserted-by":"crossref","unstructured":"Mockus, J.: On Bayesian methods for seeking the extremum. In: Proceedings of the IFIP Technical Conference, pp. 400\u2013404. Springer, Berlin, Heidelberg (1974)","DOI":"10.1007\/978-3-662-38527-2_55"},{"key":"2083_CR11","doi-asserted-by":"publisher","first-page":"148","DOI":"10.1109\/JPROC.2015.2494218","volume":"104","author":"B Shahriari","year":"2016","unstructured":"Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the human out of the loop: a review of Bayesian optimization. Proc. IEEE 104, 148\u2013175 (2016)","journal-title":"Proc. IEEE"},{"key":"2083_CR12","unstructured":"Snoek, J., Larochelle, H., Adams, R.P.: Practical Bayesian optimization of machine learning algorithms. In: Proceedings of the 25th International Conference on Neural Information Processing Systems, Vol. 2. NIPS\u201912, pp. 2951\u20132959. Curran Associates Inc., Red Hook (2012)"},{"key":"2083_CR13","unstructured":"Bergstra, J.S., Bardenet, R., Bengio, Y., K\u00e9gl, B.: Algorithms for hyper-parameter optimization. In: Shawe-Taylor, J., Zemel, R.S., Bartlett, P.L., Pereira, F., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems 24 (2011). http:\/\/papers.nips.cc\/paper\/4443-algorithms-for-hyper-parameter-optimization.pdf"},{"key":"2083_CR14","unstructured":"Snoek, J., Rippel, O., Swersky, K., Kiros, R., Satish, N., Sundaram, N., Patwary, M., Prabhat, M., Adams, R.: Scalable Bayesian optimization using deep neural networks. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 2171\u20132180. PMLR, Lille, France (2015). http:\/\/proceedings.mlr.press\/v37\/snoek15.html"},{"key":"2083_CR15","doi-asserted-by":"crossref","unstructured":"Chollet, F.: Xception: deep learning with depthwise separable convolutions. CoRR (2016). arXiv:1610.02357","DOI":"10.1109\/CVPR.2017.195"},{"issue":"2","key":"2083_CR16","doi-asserted-by":"publisher","first-page":"99","DOI":"10.1162\/106365602320169811","volume":"10","author":"KO Stanley","year":"2002","unstructured":"Stanley, K.O., Miikkulainen, R.: Evolving neural networks through augmenting topologies. Evol. Comput. 10(2), 99\u2013127 (2002). https:\/\/doi.org\/10.1162\/106365602320169811","journal-title":"Evol. Comput."},{"key":"2083_CR17","unstructured":"Kandasamy, K., Neiswanger, W., Schneider, J., P\u00f3czos, B., Xing, E.P.: Neural architecture search with Bayesian optimisation and optimal transport. In: Proceedings of the 32nd International Conference on Neural Information Processing Systems. NIPS\u201918, pp. 2020\u20132029. Curran Associates Inc., Red Hook, NY, USA (2018)"},{"key":"2083_CR18","unstructured":"Pham, H., Guan, M., Zoph, B., Le, Q., Dean, J.: Efficient neural architecture search via parameters sharing. In: Proceedings of Machine Learning Research, vol. 80, pp. 4095\u20134104. PMLR, Stockholmsm\u00e4ssan, Stockholm Sweden (2018). http:\/\/proceedings.mlr.press\/v80\/pham18a.html"},{"key":"2083_CR19","doi-asserted-by":"crossref","unstructured":"Tan, M., Chen, B., Pang, R., Vasudevan, V., Le, Q.V.: Mnasnet: platform-aware neural architecture search for mobile. CoRR (2018). arXiv:1807.11626","DOI":"10.1109\/CVPR.2019.00293"},{"issue":"55","key":"2083_CR20","first-page":"1","volume":"20","author":"T Elsken","year":"2019","unstructured":"Elsken, T., Metzen, J.H., Hutter, F.: Neural architecture search: a survey. J. Mach. Learn. Res. 20(55), 1\u201321 (2019)","journal-title":"J. Mach. Learn. Res."},{"key":"2083_CR21","doi-asserted-by":"publisher","DOI":"10.1016\/j.envsoft.2020.104954","volume":"137","author":"S Razavi","year":"2021","unstructured":"Razavi, S., Jakeman, A., Saltelli, A., Prieur, C., Iooss, B., Borgonovo, E., Plischke, E., Lo Piano, S., Iwanaga, T., Becker, W., Tarantola, S., Guillaume, J.H.A., Jakeman, J., Gupta, H., Melillo, N., Rabitti, G., Chabridon, V., Duan, Q., Sun, X., Smith, S., Sheikholeslami, R., Hosseini, N., Asadzadeh, M., Puy, A., Kucherenko, S., Maier, H.R.: The future of sensitivity analysis: an essential discipline for systems modeling and policy support. Environ. Model. Softw. 137, 104954 (2021). https:\/\/doi.org\/10.1016\/j.envsoft.2020.104954","journal-title":"Environ. Model. Softw."},{"key":"2083_CR22","first-page":"407","volume":"1","author":"IM Sobol","year":"1993","unstructured":"Sobol, I.M.: Sensitivity estimates for nonlinear mathematical models. MMCE 1, 407\u2013414 (1993)","journal-title":"MMCE"},{"issue":"15","key":"2083_CR23","doi-asserted-by":"publisher","first-page":"4349","DOI":"10.1080\/03610926.2014.901369","volume":"45","author":"J-C Fort","year":"2016","unstructured":"Fort, J.-C., Klein, T., Rachdi, N.: New sensitivity analysis subordinated to a contrast. Commun. Stat. Theory Methods 45(15), 4349\u20134364 (2016). https:\/\/doi.org\/10.1080\/03610926.2014.901369","journal-title":"Commun. Stat. Theory Methods"},{"issue":"6","key":"2083_CR24","doi-asserted-by":"publisher","first-page":"771","DOI":"10.1016\/j.ress.2006.04.015","volume":"92","author":"E Borgonovo","year":"2007","unstructured":"Borgonovo, E.: A new uncertainty importance measure. Reliab. Eng. Syst. Saf. 92(6), 771\u2013784 (2007). https:\/\/doi.org\/10.1016\/j.ress.2006.04.015","journal-title":"Reliab. Eng. Syst. Saf."},{"issue":"2","key":"2083_CR25","doi-asserted-by":"publisher","first-page":"280","DOI":"10.1016\/S0010-4655(02)00280-1","volume":"145","author":"A Saltelli","year":"2002","unstructured":"Saltelli, A.: Making best use of model evaluations to compute sensitivity indices. Comput. Phys. Commun. 145(2), 280\u2013297 (2002). https:\/\/doi.org\/10.1016\/S0010-4655(02)00280-1","journal-title":"Comput. Phys. Commun."},{"key":"2083_CR26","doi-asserted-by":"publisher","DOI":"10.1080\/00949655.2014.945932","author":"S Da Veiga","year":"2013","unstructured":"Da Veiga, S.: Global sensitivity analysis with dependence measures. J. Stat. Comput. Simul. (2013). https:\/\/doi.org\/10.1080\/00949655.2014.945932","journal-title":"J. Stat. Comput. Simul."},{"key":"2083_CR27","first-page":"229","volume":"2","author":"I Csizar","year":"1967","unstructured":"Csizar, I.: Information-type measures of difference of probability distributions and indirect observation. Stud. Sci. Math. Hung. 2, 229\u2013318 (1967)","journal-title":"Stud. Sci. Math. Hung."},{"issue":"2","key":"2083_CR28","doi-asserted-by":"publisher","first-page":"429","DOI":"10.2307\/1428011","volume":"29","author":"A M\u00fcller","year":"1997","unstructured":"M\u00fcller, A.: Integral probability metrics and their generating classes of functions. Adv. Appl. Probab. 29(2), 429\u2013443 (1997). https:\/\/doi.org\/10.2307\/1428011","journal-title":"Adv. Appl. Probab."},{"key":"2083_CR29","doi-asserted-by":"publisher","first-page":"417","DOI":"10.1137\/18M1167978","volume":"7","author":"A Spagnol","year":"2018","unstructured":"Spagnol, A., Riche, R.L., Da Veiga, S.: Global sensitivity analysis for optimization with variable selection. SIAM\/ASA J. Uncertain. Quantif. 7, 417\u2013443 (2018)","journal-title":"SIAM\/ASA J. Uncertain. Quantif."},{"key":"2083_CR30","unstructured":"Fukumizu, K., Gretton, A., Lanckriet, G.R., Sch\u00f6lkopf, B., Sriperumbudur, B.K.: Kernel choice and classifiability for rkhs embeddings of probability distributions. In: Bengio, Y., Schuurmans, D., Lafferty, J.D., Williams, C.K.I., Culotta, A. (eds.) Advances in Neural Information Processing Systems 22 (2009). http:\/\/papers.nips.cc\/paper\/3750-kernel-choice-and-classifiability-for-rkhs-embeddings-of-probability-distributions.pdf"},{"issue":"1","key":"2083_CR31","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/BF02289565","volume":"29","author":"JB Kruskal","year":"1964","unstructured":"Kruskal, J.B.: Multidimensional scaling by optimizing goodness of fit to a nonmetric hypothesis. Psychometrika 29(1), 1\u201327 (1964). https:\/\/doi.org\/10.1007\/BF02289565","journal-title":"Psychometrika"},{"issue":"4","key":"2083_CR32","doi-asserted-by":"publisher","first-page":"403","DOI":"10.1016\/0021-9991(76)90041-3","volume":"22","author":"DT Gillespie","year":"1976","unstructured":"Gillespie, D.T.: A general method for numerically simulating the stochastic time evolution of coupled chemical reactions. J. Comput. Phys. 22(4), 403\u2013434 (1976). https:\/\/doi.org\/10.1016\/0021-9991(76)90041-3","journal-title":"J. Comput. Phys."},{"key":"2083_CR33","unstructured":"Falkner, S., Klein, A., Hutter, F.: BOHB: Robust and efficient hyperparameter optimization at scale. In: Proceedings of Machine Learning Research, vol. 80, pp. 1437\u20131446. PMLR, Stockholmsm\u00e4ssan, Stockholm Sweden (2018). http:\/\/proceedings.mlr.press\/v80\/falkner18a.html"},{"key":"2083_CR34","doi-asserted-by":"publisher","unstructured":"Song, L., Smola, A., Gretton, A., Borgwardt, K.M., Bedo, J.: Supervised feature selection via dependence estimation. In: Proceedings of the 24th International Conference on Machine Learning. ICML \u201907, pp. 823\u2013830. Association for Computing Machinery, New York, NY, USA (2007). https:\/\/doi.org\/10.1145\/1273496.1273600","DOI":"10.1145\/1273496.1273600"}],"container-title":["Journal of Scientific Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10915-022-02083-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10915-022-02083-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10915-022-02083-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,2,22]],"date-time":"2023-02-22T21:09:35Z","timestamp":1677100175000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10915-022-02083-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,1,16]]},"references-count":34,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2023,3]]}},"alternative-id":["2083"],"URL":"https:\/\/doi.org\/10.1007\/s10915-022-02083-4","relation":{},"ISSN":["0885-7474","1573-7691"],"issn-type":[{"value":"0885-7474","type":"print"},{"value":"1573-7691","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,1,16]]},"assertion":[{"value":"13 November 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 September 2022","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 November 2022","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 January 2023","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"All authors certify that they have no affiliations with or involvement in any organization or entity with any financial interest or non-financial interest in the subject matter or materials discussed in this manuscript.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"All authors approve the Committee on Publication Ethics guidelines. They consent to participate.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical Approval and Consent to Participate"}},{"value":"All authors give explicit consent to submit and they obtained consent from the responsible authorities at the institutes where the work has been carried out, the CEA, Inria and the Ecole Polytechnique.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for Publication"}}],"article-number":"45"}}