F. Bachoc, G. Blanchard, and P. Neuvial, On the post selection inference constant under restricted isometry properties, Electronic Journal of Statistics, 2018.
DOI : 10.1214/18-ejs1490
URL : https://hal.archives-ouvertes.fr/hal-01772538

J. Bect, F. Bachoc, and D. Ginsbourger, A supermartingale approach to Gaussian process based sequential design of experiments. Bernoulli, forthcoming, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01351088

A. F. López-lopera, F. Bachoc, N. Durrande, and O. Roustant, Finite-dimensional Gaussian approximation with linear inequality constraints, SIAM/ASA Journal on Uncertainty Quantification, 2018.

F. Bachoc, H. Leeb, and B. M. Pötscher, Valid confidence intervals for post-model-selection predictors, Annals of Statistics, 2018.

F. Bachoc, F. Gamboa, J. M. Loubes, and N. Venet, Gaussian process regression model for distribution inputs, IEEE Transactions on Information Theory, 2017.
DOI : 10.1109/tit.2017.2762322
URL : https://hal.archives-ouvertes.fr/hal-01450002

P. Abrahamsen, A review of Gaussian random fields and correlation functions, 1997.

M. Abramowitz and I. A. Stegun, Handbook of Mathematical Functions with Formulas, Graphs, and Mathematical Tables, 1964.

E. Anderes, On the consistent separation of scale and variance for Gaussian random fields, The Annals of Statistics, vol.38, pp.870-893, 2010.

I. Andrianakis and P. G. Challenor, The effect of the nugget on Gaussian process emulators of computer models, Computational Statistics and Data Analysis, 2012.

A. B. Antognini and M. Zagoraiou, Exact optimal designs for computer experiments via Kriging metamodelling, Journal of Statistical Planning and Inference, vol.140, pp.2607-2617, 2010.

F. Bachoc, Parametric estimation of covariance function in Gaussian-process based Kriging models. Application to uncertainty quantification for computer experiments, 2013.
URL : https://hal.archives-ouvertes.fr/tel-00881002

J. Bect, D. Ginsbourger, L. Li, V. Picheny, and E. Vazquez, Sequential design of computer experiments for the estimation of a probability of failure, Statistics and Computing, vol.22, issue.3, pp.773-793, 2012.
URL : https://hal.archives-ouvertes.fr/hal-00689580

A. Belloni, V. Chernozhukov, and C. Hansen, Inference for high-dimensional sparse econometric models, Advances in Economics and Econometrics. 10th World Congress of the, vol.III, pp.245-295, 2011.

A. Belloni, V. Chernozhukov, and C. Hansen, Inference on treatment effects after selection among high-dimensional controls, The Review of Economic Studies, vol.81, pp.608-650, 2014.
DOI : 10.1093/restud/rdt044
URL : http://dspace.mit.edu/bitstream/1721.1/108404/1/Inference%20on%20Treatment%20Effects.pdf

C. Berg, J. P. Christensen, and P. Ressel, Harmonic analysis on semigroups, 1984.

R. Berk, L. Brown, A. Buja, K. Zhang, and L. Zhao, Valid post-selection inference, The Annals of Statistics, vol.41, issue.2, pp.802-837, 2013.
DOI : 10.1214/12-aos1077
URL : https://doi.org/10.1214/12-aos1077

M. Bevilacqua, A. Fassò, C. Gaetan, E. Porcu, and D. Velandia, Covariance tapering for multivariate Gaussian random fields estimation, Statistical Methods & Applications, pp.1-17, 2015.
DOI : 10.1007/s10260-015-0338-3

M. Bevilacqua, T. Faouzi, R. Furrer, and E. Porcu, Estimation and prediction using generalized wendland covariance functions under fixed domain asymptotics, Annals of Statistics, 2018.

M. Binois, Uncertainty quantification on pareto fronts and high-dimensional strategies in bayesian optimization, with applications in multi-objective automotive design, 2015.
URL : https://hal.archives-ouvertes.fr/tel-01310521

Z. I. Botev, The normal law under linear restrictions: simulation and estimation via minimax tilting, Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol.79, issue.1, pp.125-148, 2017.

A. D. Bull, Convergence rates of efficient global optimization algorithms, Journal of Machine Learning Research, vol.12, pp.2879-2904, 2011.

Y. Cao and D. J. Fleet, Generalized product of experts for automatic and principled fusion of Gaussian process predictions, Modern Nonparametrics 3: Automating the Learning Pipeline workshop at NIPS, 2014.

L. Castera, H. Chan, M. Arrese, N. Afdhal, P. Bedossa et al., EASL-ALEH clinical practice guidelines: non-invasive tests for evaluation of liver disease severity and prognosis, Journal of Hepatology, vol.63, pp.237-264, 2015.

A. Charkhi and G. Claeskens, Asymptotic post-selection inference for the Akaike information criterion, Biometrika, 2018.
DOI : 10.1093/biomet/asy018

V. Chernozhukov, D. Chetverikov, and K. Kato, Gaussian approximations and multiplier bootstrap for maxima of sums of high-dimensional random vectors, The Annals of Statistics, vol.41, issue.6, pp.2786-2819, 2013.

C. Chevalier, J. Bect, D. Ginsbourger, E. Vazquez, V. Picheny et al., Fast parallel Kriging-based stepwise uncertainty reduction with application to the identification of an excursion set, Technometrics, vol.56, issue.4, pp.455-465, 2014.
URL : https://hal.archives-ouvertes.fr/hal-00641108

H. Cohn and A. Kumar, Universally optimal distribution of points on spheres, Journal of the American Mathematical Society, vol.20, issue.1, pp.99-148, 2007.

H. Cohn, A. Kumar, and G. Minton, Optimal simplices and codes in projective spaces, Geometry & Topology, vol.20, issue.3, pp.1289-1357, 2016.
DOI : 10.2140/gt.2016.20.1289
URL : http://arxiv.org/pdf/1308.3188

J. H. Conway, R. H. Hardin, and N. J. Sloane, Packing lines, planes, etc.: Packings in grassmannian spaces, Experimental mathematics, vol.5, issue.2, pp.139-159, 1996.

A. Cousin, H. Maatouk, and D. Rullière, Kriging of financial term-structures, European Journal of Operational Research, vol.255, issue.2, pp.631-648, 2016.
DOI : 10.1016/j.ejor.2016.05.057
URL : https://hal.archives-ouvertes.fr/hal-01206388

N. Cressie and S. Lahiri, The asymptotic distribution of REML estimators, Journal of Multivariate Analysis, vol.45, pp.217-233, 1993.
DOI : 10.1006/jmva.1993.1034
URL : https://doi.org/10.1006/jmva.1993.1034

S. D. Veiga and A. Marrel, Gaussian process modeling with inequality constraints, Toulouse Mathématiques, vol.21, issue.3, p.2012

M. Deisenroth and J. Ng, Distributed Gaussian processes, Proceedings of the 32nd International Conference on Machine Learning, vol.37, 2015.

J. Du, H. Zhang, and V. S. Mandrekar, Fixed-domain asymptotic properties of tapered maximum likelihood estimators, The Annals of Statistics, vol.37, issue.6A, pp.3330-3361, 2009.
DOI : 10.1214/08-aos676
URL : https://doi.org/10.1214/08-aos676

O. Dubrule, Cross validation of Kriging in a unique neighborhood, Mathematical Geology, vol.15, pp.687-699, 1983.

R. M. Dudley, Real analysis and probability, 2002.

M. Emmerich, K. Giannakoglou, and B. Naujoks, Single-and multiobjective optimization assisted by Gaussian random field metamodels, IEEE Transactions on Evolutionary Computation, vol.10, issue.4, 2006.
DOI : 10.1109/tevc.2005.859463

J. Fan and R. Li, Variable selection via nonconcave penalized likelihood and its oracle properties, Journal of the American Statistical Association, vol.96, pp.1348-1360, 2001.
DOI : 10.1198/016214501753382273

P. Feliot, J. Bect, and E. Vazquez, A Bayesian approach to constrained single-and multiobjective optimization, Journal of Global Optimization, vol.67, issue.1, pp.97-133, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01207679

W. Fithian, D. Sun, and J. Taylor, Optimal inference after model selection, 2015.

S. R. Flaxman, Y. Wang, and A. J. Smola, Who supported obama in 2012?: Ecological inference through distribution regression, Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp.289-298, 2015.

A. Forrester, A. Sobester, and A. Keane, Engineering design via surrogate modelling: a practical guide, 2008.

S. Foucart and H. Rauhut, A mathematical introduction to compressive sensing, 2013.

R. Furrer and S. R. Sain, SPAM: A sparse matrix R package with emphasis on MCMC methods for Gaussian Markov random fields, Journal of Statistical Software, vol.36, issue.10, pp.1-25, 2010.

R. Furrer, M. G. Genton, and D. Nychka, Covariance tapering for interpolation of large spatial datasets, Journal of Computational and Graphical Statistics, vol.15, issue.3, pp.502-523, 2006.

F. Gaudier, URANIE: The CEA DEN uncertainty and sensitivity platform, Procedia-Social and Behavioral Sciences, vol.2, pp.7660-7661, 2010.

M. G. Genton and W. Kleiber, Cross-covariance functions for multivariate geostatistics, Statistical Science, vol.30, issue.2, pp.147-163, 2015.

A. Genz, Numerical computation of multivariate normal probabilities, Journal of Computational and Graphical Statistics, vol.1, pp.141-150, 1992.

D. Ginsbourger, J. Baccou, C. Chevalier, N. Garland, F. Perales et al., Bayesian adaptive reconstruction of profile optima and optimizers
URL : https://hal.archives-ouvertes.fr/hal-00920154

S. Asa, Journal on Uncertainty Quantification, vol.2, issue.1, pp.490-510, 2014.

T. Gneiting, W. Kleiber, and M. Schlather, Matérn cross-covariance functions for multivariate random fields, Journal of the American Statistical Association, vol.105, issue.491, pp.1167-1177, 2010.

S. Golchi, D. R. Bingham, H. Chipman, and D. A. Campbell, Monotone emulation of computer experiments, SIAM/ASA Journal on Uncertainty Quantification, vol.3, issue.1, pp.370-392, 2015.

R. Gramacy, G. Gray, S. L. Digabel, H. Lee, P. Ranjan et al., Modeling an augmented Lagrangian for blackbox constrained optimization, Technometrics (with discussion), vol.58, issue.1, pp.1-11, 2016.

A. Guyader, N. Hengartner, and E. Matzner-løber, Simulation and estimation of extreme quantiles and extreme probabilities, Applied Mathematics & Optimization, vol.64, issue.2, pp.171-196, 2011.
URL : https://hal.archives-ouvertes.fr/hal-00911891

T. Hangelbroek, F. J. Narcowich, and J. D. Ward, Kernel approximation on manifolds i: bounding the Lebesgue constant, SIAM Journal on Mathematical Analysis, vol.42, issue.4, pp.1732-1760, 2010.

J. Hensman, N. Fusi, and N. D. Lawrence, Gaussian processes for big data, Uncertainty in Artificial Intelligence, pp.282-290, 2013.

G. E. Hinton, Training products of experts by minimizing contrastive divergence, Neural computation, vol.14, issue.8, pp.1771-1800, 2002.

S. G. , Hoggar. t-designs in projective spaces, European Journal of Combinatorics, vol.3, issue.3, pp.233-254, 1982.

I. Ibragimov and Y. Rozanov, Gaussian Random Processes, 1978.

L. Jaupi, Variable selection methods for multivariate process monitoring, Proceedings of the World Congress of Engineering, vol.II, pp.1116-1120, 2014.
URL : https://hal.archives-ouvertes.fr/hal-01126456

D. Jones, M. Schonlau, and W. Welch, Efficient global optimization of expensive black box functions, Journal of Global Optimization, vol.13, pp.455-492, 1998.

P. Kabaila and H. Leeb, On the large-sample minimal coverage probability of confidence intervals after model selection, Journal of the American Statistical Association, vol.101, pp.619-629, 2006.

C. Kaufman and B. Shaby, The role of the range parameter for estimation and prediction in geostatistics, Biometrika, vol.100, pp.473-484, 2013.

C. G. Kaufman, M. J. Schervish, and D. W. Nychka, Covariance tapering for likelihood-based estimation in large spatial data sets, Journal of the American Statistical Association, vol.103, issue.484, pp.1545-1555, 2008.

T. Kawano, K. Hanson, S. Frankle, P. Talou, M. Chadwick et al., Evaluation and propagation of the 239pu fission cross-section uncertainties using a monte carlo technique, Nuclear Science and Engineering, vol.153, issue.1, pp.1-7, 2006.

A. K. Kuchibhotla, L. D. Brown, A. Buja, E. I. George, and L. Zhao, A model free perspective for linear regression: Uniform-in-model bounds for post selection inference, 2018.

A. K. Kuchibhotla, L. D. Brown, A. Buja, E. I. George, and L. Zhao, Valid post-selection inference in assumption-lean linear regression, 2018.

J. B. Lasserre and E. Pauwels, The empirical christoffel function in statistics and machine learning, 2017.

T. , L. Gouic, and J. Loubes, Existence and consistency of wasserstein barycenters. Probability Theory and Related Fields, vol.168, pp.901-917, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01163262

L. , L. Gratiet, and J. Garnier, Asymptotic analysis of the learning curve for gaussian process regression, Machine Learning, pp.1-27, 2014.
URL : https://hal.archives-ouvertes.fr/hal-01171716

J. D. Lee and J. E. Taylor, Exact post model selection inference for marginal screening, Advances in Neural Information Processing Systems, vol.27, pp.136-144, 2014.

J. D. Lee, D. L. Sun, Y. Sun, and J. E. Taylor, Exact post-selection inference, with application to the lasso, The Annals of Statistics, vol.44, issue.3, pp.907-927, 2016.

H. Leeb and B. M. Pötscher, Model selection and inference: Facts and fiction, Econometric Theory, vol.21, pp.21-59, 2005.

H. Leeb and B. M. Pötscher, Performance limits for estimators of the risk or distribution of shrinkage-type estimators, and some general lower risk-bound results, Econometric Theory, vol.22, pp.69-97, 2006.

H. Leeb and B. M. Pötscher, Model selection, Handbook of Financial Time Series, pp.785-821, 2008.

W. Loh, Fixed domain asymptotics for a subclass of Matérn type Gaussian random fields, The Annals of Statistics, vol.33, pp.2344-2394, 2005.

W. Loh and T. Lam, Estimating structured correlation matrices in smooth Gaussian random field models, The Annals of Statistics, vol.28, pp.880-904, 2000.

H. Maatouk and X. Bay, Gaussian process emulators for computer experiments with inequality constraints, Mathematical Geosciences, vol.49, issue.5, pp.557-582, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01096751

K. Mardia and R. Marshall, Maximum likelihood estimation of models for residual covariance in spatial regression, Biometrika, vol.71, pp.135-146, 1984.

G. Matheron, La Théorie des Variables Régionalisées et ses Applications. Fasicule 5 in Les Cahiers du, 1970.

J. B. Mockus, V. Tiesis, and A. ?ilinskas, The application of Bayesian methods for seeking the extremum, Towards Global Optimization, vol.2, pp.117-129, 1978.

T. Muehlenstaedt, J. Fruth, and O. Roustant, Computer experiments with functional inputs and scalar outputs by a norm-based approach, Statistics and Computing, pp.1-15, 2016.
URL : https://hal.archives-ouvertes.fr/emse-01506232

K. P. Murphy, Machine Learning: A Probabilistic Perspective (Adaptive Computation And Machine Learning Series, 2012.

S. Nanty, C. Helbert, A. Marrel, N. Pérot, and C. Prieur, Sampling, metamodeling, and sensitivity analysis of numerical simulators with functional stochastic inputs, SIAM/ASA Journal on Uncertainty Quantification, vol.4, issue.1, pp.636-659, 2016.

T. Nickson, T. Gunter, C. Lloyd, M. A. Osborne, and S. Roberts, Blitzkriging: Kroneckerstructured stochastic Gaussian processes, 2015.

A. B. Owen and C. Prieur, On Shapley value for measuring importance of dependent inputs
URL : https://hal.archives-ouvertes.fr/hal-01379188

S. Asa, Journal on Uncertainty Quantification, vol.5, issue.1, pp.986-1002, 2017.

A. Pakman and L. Paninski, Exact Hamiltonian Monte Carlo for truncated multivariate Gaussians, Journal of Computational and Graphical Statistics, vol.23, issue.2, pp.518-542, 2014.

R. Paulo, G. Garcia-donato, and J. Palomo, Calibration of computer models with multivariate output, Computational Statistics and Data Analysis, vol.56, pp.3959-3974, 2012.

V. Picheny, A stepwise uncertainty reduction approach to constrained global optimization, Proceedings of the 17 th International Conference on Artificial Intelligence and Statistics (AISTATS), 2014.

V. Picheny, D. Ginsbourger, O. Roustant, R. Haftka, and N. Kim, Adaptive designs of experiments for accurate approximation of target regions, Journal of Mechanical Design, vol.132, issue.7, 2010.

B. Póczos, A. Singh, A. Rinaldo, and L. Wasserman, Distribution-free distribution regression, Proceedings of the 16th International Conference on Artificial Intelligence and Statistics, vol.31, pp.507-515, 2013.

B. M. Pötscher, Confidence sets based on sparse estimators are necessarily large, Sankhya, vol.71, pp.1-18, 2009.

G. Radulescu, D. E. Mueller, and J. C. Wagner, Sensitivity and uncertainty analysis of commercial reactor criticals for burnup credit, Nuclear Technology, vol.167, issue.2, pp.268-287, 2009.

P. Ranjan, D. Bingham, and G. Michailidis, Sequential experiment design for contour estimation from complex computer codes, Technometrics, vol.50, issue.4, pp.527-541, 2008.

C. Rasmussen and C. Williams, Gaussian Processes for Machine Learning, 2006.

J. Riihimäki and A. Vehtari, Gaussian processes with monotonicity information, Journal of Machine Learning Research: Workshop and Conference Proceedings, vol.9, pp.645-652, 2010.

L. Roche and M. Pelletier, Modelling of the thermomechanical and physical processes in FR fuel pins using the GERMINAL code, MOX Fuel Cycle Technologies for Medium and Long Term Deployment, p.322, 2000.

O. Roustant, D. Ginsbourger, Y. Deville, and . Dicekriging, DiceOptim: Two R packages for the analysis of computer experiments by Kriging-based metamodeling and optimization, Journal of Statistical Software, vol.51, issue.1, 2012.
URL : https://hal.archives-ouvertes.fr/hal-00495766

H. Rue and L. Held, Gaussian Markov random fields, Theory and applications, 2005.

T. Santner, B. Williams, and W. Notz, The Design and Analysis of Computer Experiments, 2003.

B. A. Shaby and D. Ruppert, Tapered covariance: Bayesian estimation and asymptotics, Journal of Computational and Graphical Statistics, vol.21, issue.2, pp.433-452, 2012.

B. Shahriari, K. Swersky, Z. Wang, R. Adams, and N. De-freitas, Taking the human out of the loop: A review of bayesian optimization, Proceedings of the IEEE, vol.104, issue.1, pp.148-175, 2016.

T. Souders and G. Stenbakken, Cutting the high cost of testing, IEEE Spectrum, vol.28, pp.48-51, 1991.

N. Srinivas, K. A. , S. Kakade, and M. Seeger, Information-theoretic regret bounds for Gaussian process optimization in the bandit setting, IEEE Transactions on Information Theory, vol.58, pp.3250-3265, 2012.

M. Stein, Asymptotically efficient prediction of a random field with a misspecified covariance function, The Annals of Statistics, vol.16, pp.55-63, 1988.

M. Stein, Bounds on the efficiency of linear predictions using an incorrect covariance function, The Annals of Statistics, vol.18, pp.1116-1138, 1990.

M. Stein, Uniform asymptotic optimality of linear predictions of a random field using an incorrect second-order structure, The Annals of Statistics, vol.18, pp.850-872, 1990.

M. Stein, Interpolation of Spatial Data: Some Theory for Kriging, 1999.

M. L. Stein, Statistical properties of covariance tapers, Journal of Computational and Graphical Statistics, vol.22, issue.4, pp.866-885, 2013.

M. L. Stein, Limitations on low rank approximations for covariance matrices of spatial data, Spatial Statistics, vol.8, pp.1-19, 2014.

S. Sundararajan and S. Keerthi, Predictive approaches for choosing hyperparameters in Gaussian processes, Neural Computation, vol.13, pp.1103-1118, 2001.

J. Taylor and Y. Benjamini, RestrictedMVN: multivariate normal restricted by affine constraints, p.2, 2017.

J. Taylor and R. Tibshirani, Post-selection inference for l1-penalized likelihood models, Canadian Journal of Statistics, pp.1-21, 2017.

R. Tibshirani, Regression shrinkage and selection via the lasso, Journal of the Royal Statistical Society. Series B (Methodological), pp.267-288, 1996.

R. J. Tibshirani, J. Taylor, R. Lockhart, and R. Tibshirani, Exact post-selection inference for sequential regression procedures, Journal of the American Statistical Association, vol.111, issue.514, pp.600-620, 2016.

R. J. Tibshirani, A. Rinaldo, R. Tibshirani, and L. Wasserman, Uniform asymptotic inference and the bootstrap after model selection, The Annals of Statistics, vol.46, issue.3, pp.1255-1287, 2018.

V. Tresp, A bayesian committee machine, Neural Computation, vol.12, issue.11, pp.2719-2741, 2000.

S. Van-de-geer, P. Bühlmann, Y. Ritov, and R. Dezeure, On asymptotically optimal confidence regions and tests for high-dimensional models, The Annals of Statistics, vol.42, pp.1166-1202, 2014.

E. Vazquez and J. Bect, A sequential Bayesian algorithm to estimate a probability of failure, 15th IFAC Symposium on System Identification, 2009.
URL : https://hal.archives-ouvertes.fr/hal-00368158

E. Vazquez and J. Bect, Convergence properties of the expected improvement algorithm with fixed mean and covariance functions, Journal of Statistical Planning and inference, vol.140, issue.11, pp.3088-3095, 2010.
URL : https://hal.archives-ouvertes.fr/hal-00217562

E. Vazquez and J. Bect, Pointwise consistency of the kriging predictor with known mean and covariance functions, mODa 9-Advances in Model-Oriented Design and Analysis, pp.221-228, 2010.
URL : https://hal.archives-ouvertes.fr/hal-00440827

H. Wackernagel, Multivariate Geostatistics: An Introduction with Applications, 2003.

G. Wahba, Spline Models for Observational Data, Society for Industrial and Applied Mathematics, 1990.

H. Wang, G. Lin, and J. Li, Gaussian process surrogates for failure detection: a Bayesian experimental design approach, Journal of Computational Physics, vol.313, pp.247-259, 2016.

R. W. Wedderburn, On the existence and uniqueness of the maximum likelihood estimates for certain generalized linear models, Biometrika, vol.63, issue.1, pp.27-32, 1976.

B. J. Williams, T. J. Santner, and W. I. Notz, Sequential design of computer experiments to minimize integrated response functions, Statistica Sinica, vol.10, pp.1133-1152, 2000.

D. Yarotsky, Examples of inconsistency in optimization by expected improvement, Journal of Global Optimization, vol.56, issue.4, pp.1773-1790, 2013.

Z. Ying, Asymptotic properties of a maximum likelihood estimator with data from a Gaussian process, Journal of Multivariate Analysis, vol.36, pp.280-296, 1991.

Z. Ying, Maximum likelihood estimation of parameters under a spatial sampling scheme, The Annals of Statistics, vol.21, pp.1567-1590, 1993.

C. Zhang, Nearly unbiased variable selection under minimax concave penalty, The Annals of Statistics, vol.38, issue.2, pp.894-942, 2010.

C. Zhang and S. Zhang, Confidence intervals for low dimensional parameters in high dimensional linear models, Journal of the Royal Statistical Society. Series B (Methodological), vol.76, pp.217-242, 2014.

H. Zhang, Inconsistent estimation and asymptotically equivalent interpolations in model-based geostatistics, Journal of the American Statistical Association, vol.99, pp.250-261, 2004.

H. Zhang and W. Cai, When doesn't cokriging outperform kriging? Statistical Science, vol.30, pp.176-180, 2015.

H. Zhang and Y. Wang, Kriging and cross validation for massive spatial data, Environmetrics, vol.21, pp.290-304, 2010.

H. Zhang and D. Zimmerman, Toward reconciling two asymptotic frameworks in spatial statistics, Biometrika, vol.92, pp.921-936, 2005.

K. Zhang, Spherical cap packing asymptotics and rank-extreme detection, IEEE Transactions on Information Theory, vol.63, issue.7, pp.4572-4584, 2017.

M. Zuluaga, A. Krause, G. Sergent, and M. Püschel, Active learning for level set estimation, International Joint Conference on Artificial Intelligence (IJCAI), 2013.