Fetching the paper…
Reading the bibliography…
Efficient global optimization is a widely used method for optimizing expensive black-box functions such as tuning hyperparameter, and designing new material, etc.
Uspekhi Matematicheskikh Nauk 14
Kolmogorov, A.N., Tikhomirov, V.M.: ε \varepsilon -entropy and ε \varepsilon -capacity of sets in function spaces · 1959
Earlier work this paper cites.
Bulletin of the American Mathematical Society 72
Lorentz, G.: Metric entropy and approximation · 1966
Earlier work this paper cites.
Wiley-Interscience (1983)
Nemirovskij, A.S., Yudin, D.B.: Problem complexity and method efficiency in optimization · 1983
Earlier work this paper cites.
SIAM (1990)
Wahba, G.: Spline models for observational data · 1990
Earlier work this paper cites.
SIAM Journal on Control and Optimization 33
Agrawal, R.: The continuum-armed bandit problem · 1995
Earlier work this paper cites.
Cambridge Univ Pr (1996)
Edmunds, D.E., Triebel, H.: Function spaces, entropy numbers, differential operators, vol. 120 · 1996
Earlier work this paper cites.
Journal of Global Optimization 10
Locatelli, M.: Bayesian algorithms for one-dimensional global optimization · 1997
Earlier work this paper cites.
Journal of Global Optimization 13
Jones, D.R., Schonlau, M., Welch, W.J.: Efficient global optimization of expensive black-box functions · 1998
Earlier work this paper cites.
Bulletin of the American mathematical society 39
Cucker, F., Smale, S.: On the mathematical foundations of learning · 2002
Earlier work this paper cites.
Journal of Complexity 18
Zhou, D.X.: The covering number in learning theory · 2002
Earlier work this paper cites.
IEEE Transactions on Information Theory 49
Zhou, D.X.: Capacity of reproducing kernel spaces in learning theory · 2003
Earlier work this paper cites.
Cambridge university press (2004)
Wendland, H.: Scattered data approximation, vol. 17 · 2004
Earlier work this paper cites.
Mathematical Programming 106
Wächter, A., Biegler, L.T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming · 2006
Earlier work this paper cites.
European Mathematical Society (2008)
Novak, E., Woźniakowski, H.: Tractability of Multivariate Problems: Standard information for functionals, vol. 2 · 2008
Earlier work this paper cites.
Journal of Statistical Planning and Inference 140
Vazquez, E., Bect, J.: Convergence properties of the expected improvement algorithm with fixed mean and covariance functions · 2010
Cited alongside, same era.
In: Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, pp. 273–280. JMLR Workshop and Conference Proceedings (2010)
Grünewälder, S., Audibert, J.Y., Opper, M., Shawe-Taylor, J.: Regret bounds for Gaussian process bandit problems · 2010
Cited alongside, same era.
INFORMS Journal on Computing 23
Negoescu, D.M., Frazier, P.I., Powell, W.B.: The knowledge-gradient algorithm for sequencing experiments in drug discovery · 2011
Cited alongside, same era.
Journal of Machine Learning Research 12
Bull, A.D.: Convergence rates of efficient global optimization algorithms · 2011
Cited alongside, same era.
SIAM Journal on Control and Optimization 49
Frazier, P.I., Powell, W.B.: Consistency of sequential Bayesian sampling policies · 2011
Cited alongside, same era.
Journal of Machine Learning Research 17
Russo, D., Van Roy, B.: An information-theoretic analysis of thompson sampling · 2016
Later among the works it cites.
In: 2017 IEEE 56th Annual Conference on Decision and Control (CDC), pp. 5168–5173. IEEE (2017)
Bansal, S., Calandra, R., Xiao, T., Levine, S., Tomlin, C.J.: Goal-driven dynamics learning via Bayesian optimization · 2017
Later among the works it cites.
In: Conference on Learning Theory, pp. 1723–1742. PMLR (2017)
Scarlett, J., Bogunovic, I., Cevher, V.: Lower bounds on regret for noisy Gaussian process bandit optimization · 2017
Later among the works it cites.
Journal of Approximation Theory 215
Steinwart, I.: A short note on the comparison of interpolation widths, entropy numbers, and kolmogorov widths · 2017
Later among the works it cites.
Lecture Notes for ECE598YW (UIUC) 16
Wu, Y.: Lecture notes on information-theoretic methods for high-dimensional statistics · 2017
Later among the works it cites.
In: International Conference on Machine Learning, pp. 4500–4508. PMLR (2018)
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization · 2012
Cited alongside, same era.
IEEE Transactions on Information Theory 58
Srinivas, N., Krause, A., Kakade, S.M., Seeger, M.W.: Information-theoretic regret bounds for Gaussian process optimization in the bandit setting · 2012
Cited alongside, same era.
In: Proceedings of the 29th International Coference on Machine Learning, pp. 955–962 (2012)
De Freitas, N., Smola, A.J., Zoghi, M.: Exponential regret bounds for Gaussian process bandits with deterministic observations · 2012
Cited alongside, same era.
Journal of machine learning research 13
Raskutti, G., J Wainwright, M., Yu, B.: Minimax-optimal rates for sparse additive models over kernel classes via convex programming · 2012
Cited alongside, same era.
http://github.com/SheffieldML/GPy
GPy: GPy: A Gaussian process framework in python · 2012
Cited alongside, same era.
arXiv preprint arXiv:1406.7758 (2014)
Wang, Z., de Freitas, N.: Theoretical analysis of Bayesian optimisation with unknown Gaussian process hyper-parameters · 2014
Cited alongside, same era.
In: International Conference on Machine Learning, pp. 2171–2180. PMLR (2015)
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 · 2015
Cited alongside, same era.
Scarlett, J.: Tight regret bounds for Bayesian optimization in one dimension · 2018
Later among the works it cites.
Lecture Notes, January 10
Mairal, J., Vert, J.P.: Machine learning with kernel methods · 2018
Later among the works it cites.
Advances in Neural Information Processing Systems 32
Ray Chowdhury, S., Gopalan, A.: Bayesian optimization under heavy-tailed payoffs · 2019
Later among the works it cites.
Mathematical Programming Computation 11
Andersson, J.A., Gillis, J., Horn, G., Rawlings, J.B., Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control · 2019
Later among the works it cites.
arXiv preprint arXiv:2110.07479 (2021)
Xu, W., Jones, C.N., Svetozarevic, B., Laughman, C.R., Chakrabarty, A.: VABO: Violation-aware Bayesian Optimization for closed-loop control performance optimization with unmodeled constraints · 2021
Later among the works it cites.
Advances in Neural Information Processing Systems 34
Vakili, S., Bouziani, N., Jalali, S., Bernacchia, A., Shiu, D.s.: Optimal order simple regret for Gaussian process bandits · 2021
Later among the works it cites.
In: International Conference on Machine Learning, pp. 1216–1226. PMLR (2021)
Cai, X., Scarlett, J.: On lower bounds for standard and robust Gaussian process bandit optimization · 2021
Later among the works it cites.
Automatica 134
Maddalena, E.T., Scharnhorst, P., Jones, C.N.: Deterministic error bounds for kernel-based learning techniques under bounded noise · 2021
Later among the works it cites.