-
1
-
-
0041966002
-
Using confidence bounds for exploitation-exploration trade-offs
-
Auer, Peter. Using Confidence Bounds for Exploitation-exploration Trade-offs. J. Mack. Learn. Res., 2003.
-
(2003)
J. Mack. Learn. Res.
-
-
Auer, P.1
-
3
-
-
85162384813
-
Algorithms for hyper-parameter optimization
-
Bergstra, James S., Bardenet, Remi, Bengio, Yoshua, and Kegl, Balazs. Algorithms for Hyper-Parameter Optimization. In Advances in Neural Information Processing Systems, 2011.
-
(2011)
Advances in Neural Information Processing Systems
-
-
Bergstra, J.S.1
Remi, B.2
Yoshua, B.3
Kegl, B.4
-
5
-
-
84969502516
-
A tutorial on Bayesian optimization of expensive cost functions, with application to active user modeling and hierarchical reinforcement learning
-
Brochu, Eric, Cora, Vlad M., and de Freitas, Nando. A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. CoRR, 2010.
-
(2010)
CoRR
-
-
Eric, B.1
Cora, V.M.2
De Freitas, N.3
-
6
-
-
80555140070
-
Convergence rates of efficient global optimization algorithms
-
Bull, Adam D. Convergence Rates of Efficient Global Optimization Algorithms. Journal of Machine Learning Research, 2011.
-
(2011)
Journal of Machine Learning Research
-
-
Bull, A.D.1
-
7
-
-
84867136616
-
Joint optimization and variable selection of high-dimensional Gaussian processes
-
Chen, Bo, Castro, Rui, and Krause, Andreas. Joint Optimization and Variable Selection of High-dimensional Gaussian Processes. In Int'l Conference on Machine Learning, 2012.
-
(2012)
Int'l Conference on Machine Learning
-
-
Bo, C.1
Rui, C.2
Andreas, K.3
-
10
-
-
84867478719
-
Learning where to attend with deep architectures for image tracking
-
Denil, Misha, Bazzani, Loris, Larochelle, Hugo, and de Freitas, Nando. Learning Where to Attend with Deep Architectures for Image Tracking. Neural Corn-put., 2012.
-
(2012)
Neural Cornput
-
-
Misha, D.1
Loris, B.2
Hugo, L.3
De Freitas, N.4
-
13
-
-
33847339319
-
Posterior consistency of Gaussian process prior for nonparametric binary regression
-
Ghosal, Subhashis and Roy, Anindya. Posterior consistency of Gaussian process prior for nonparametric binary regression". Annals of Statistics, 2006.
-
(2006)
Annals of Statistics
-
-
Subhashis, G.1
Anindya, R.2
-
14
-
-
84969498219
-
Bayesian optimization for synthetic gene design
-
Gonzalez, Javier, Longworth, Joseph, James, David, and Lawrence, Neil. Bayesian Optimization for Synthetic Gene Design. In NIPS Workshop on Bayesian Optimization in Academia and Industry, 2014.
-
(2014)
NIPS Workshop on Bayesian Optimization in Academia and Industry
-
-
Javier, G.1
Joseph, L.2
James, D.3
Lawrence, N.4
-
15
-
-
0003624357
-
-
Springer Series in Statistics
-
Gyorfi, Laszlo, Kohler, Micael, Krzyzak, Adam, and Walk, Harro. A Distribution Free Theory of Nonparametric Regression. Springer Series in Statistics, 2002.
-
(2002)
A Distribution Free Theory of Nonparametric Regression
-
-
Laszlo, G.1
Micael, K.2
Adam, K.3
Walk, H.4
-
18
-
-
78649510817
-
Automated antenna design with evolutionary algorithms
-
Hornby, G. S., Globus, A., Linden, D.S., and Lohn, J.D. Automated Antenna Design with Evolutionary Algorithms. American Institute of Aeronautics and Astronautics, 2006.
-
(2006)
American Institute of Aeronautics and Astronautics
-
-
Hornby, G.S.1
Globus, A.2
Linden, D.S.3
Lohn, J.D.4
-
22
-
-
84880890296
-
Automatic gait optimization with Gaussian process regression
-
Lizotte, Daniel, Wang, Tao, Bowling, Michael, and Schuur-mans, Dale. Automatic gait optimization with gaussian process regression. In in Proc. ofUCAI, pp. 944-949, 2007.
-
(2007)
Proc. of UCAI
, pp. 944-949
-
-
Lizotte, D.1
Wang, T.2
Bowling, M.3
Schuur-Mans, D.4
-
23
-
-
84969553582
-
Active pointillistic pattern search
-
Ma, Yifei, Sutherland, Dougal J., Garnett, Roman, and Schneider, Jeff G. Active Pointillistic Pattern Search. In International Conference on Artificial Intelligence and Statistics, AISTATS, 2015.
-
(2015)
International Conference on Artificial Intelligence and Statistics, AISTATS
-
-
Yifei, M.1
Sutherland, D.J.2
Roman, G.3
Schneider, J.G.4
-
24
-
-
85017445853
-
Adaptive MCMC with Bayesian optimization
-
Mahendran, Nimalan, Wang, Ziyu, Hamze, Firas, and de Freitas, Nando. Adaptive MCMC with Bayesian Optimization. In Artificial Intelligence and Statistics, 2012.
-
(2012)
Artificial Intelligence and Statistics
-
-
Mahendran, N.1
Wang, Z.2
Hamze, F.3
De Freitas, N.4
-
25
-
-
51349157486
-
Active policy learning for robot planning and exploration under uncertainty
-
Martinez-Cantin, R., de Freitas, N., Doucet, A., and Castel-lanos, J. Active Policy Learning for Robot Planning and Exploration under Uncertainty. In Proceedings of Robotics: Science and Systems, 2007.
-
(2007)
Proceedings of Robotics: Science and Systems
-
-
Martinez-Cantin, R.1
De Freitas, N.2
Doucet, A.3
Castel-Lanos, J.4
-
26
-
-
0026189315
-
Bayesian approach to global optimization and application to multiobjective and constrained problems
-
Mockus, J.B. and Mockus, L.J. Bayesian approach to global optimization and application to multiobjective and constrained problems. Journal of Optimization Theory and Applications, 1991.
-
(1991)
Journal of Optimization Theory and Applications
-
-
Mockus, J.B.1
Mockus, L.J.2
-
27
-
-
0012499686
-
Application of Bayesian approach to numerical methods of global and stochastic optimization
-
Mockus, Jonas. Application of Bayesian approach to numerical methods of global and stochastic optimization. Journal of Global Optimization, 1994.
-
(1994)
Journal of Global Optimization
-
-
Mockus, J.1
-
28
-
-
84877774592
-
Active learning of model evidence using Bayesian quadrature
-
Osborne, M., Duvenaud, D., Garnett, R., Rasmussen, C, Roberts, S., and Ghahramani, Z. Active Learning of Model Evidence Using Bayesian Quadrature. In Neural Information Processing Systems (NIPS), 2012.
-
(2012)
Neural Information Processing Systems (NIPS
-
-
Osborne, M.1
Duvenaud, D.2
Garnett, R.3
Rasmussen, C.4
Roberts, S.5
Ghahramani, Z.6
-
31
-
-
70350092487
-
Sparse additive models
-
Ravikumar, Pradeep, Lafferty, John, Liu, Han, and Wasser-man, Larry. Sparse Additive Models. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 2009.
-
(2009)
Journal of the Royal Statistical Society: Series B (Statistical Methodology
-
-
Ravikumar, P.1
Lafferty, J.2
Liu, H.3
Wasserman, L.4
-
34
-
-
77956501313
-
Gaussian process optimization in the bandit setting: No regret and experimental design
-
Srinivas, Niranjan, Krause, Andreas, Kakade, Sham, and Seeger, Matthias. Gaussian Process Optimization in the Bandit Setting: No Regret and Experimental Design. In International Conference on Machine Learning, 2010.
-
(2010)
International Conference on Machine Learning
-
-
Niranjan, S.1
Andreas, K.2
Sham, K.3
Seeger, M.4
-
35
-
-
33845429601
-
Cosmological constraints from the SDSS luminous red galaxies
-
December
-
Tegmark et al, M. Cosmological Constraints from the SDSS Luminous Red Galaxies. Physical Review, December 2006.
-
(2006)
Physical Review
-
-
Tegmark, M.1
-
36
-
-
0001395850
-
On the likelihood that one unknown probability exceeds another in view of the evidence of two samples
-
Thompson, W. R. On the Likelihood that one Unknown Probability Exceeds Another in View of the Evidence of Two Samples. Biometrika, 1933.
-
(1933)
Biometrika
-
-
Thompson, W.R.1
-
38
-
-
84896058897
-
Bayesian optimization in high dimensions via random embeddings
-
Wang, Ziyu, Zoghi, Masrour, Hutter, Frank, Matheson, David, and de Freitas, Nando. Bayesian Optimization in High Dimensions via Random Embeddings. In International Joint Conference on Artificial Intelligence, 2013.
-
(2013)
International Joint Conference on Artificial Intelligence
-
-
Wang, Z.1
Zoghi, M.2
Hutter, F.3
Matheson, D.4
De Freitas, N.5
-
39
-
-
84897558007
-
Making a science of model search: Hyperparameter optimization in hundreds of dimensions for vision architectures
-
Yamins, Daniel, Tax, David, and Bergstra, James S. Making a Science of Model Search: Hyperparameter Optimization in Hundreds of Dimensions for Vision Architectures. In International Conference on Machine Learning, 2013.
-
(2013)
International Conference on Machine Learning
-
-
Yamins, D.1
Tax, D.2
Bergstra, J.S.3
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