-
1
-
-
84869826137
-
A tutorial on Bayesian optimization of expensive cost functions, with application to active user modeling and hierarchical reinforcement learning
-
E. Brochu, V. Cora, and N. de Freitas. A tutorial on Bayesian optimization of expensive cost functions, with application to active user modeling and hierarchical reinforcement learning. CoRR, 2010.
-
(2010)
CoRR
-
-
Brochu, E.1
Cora, V.2
De Freitas, N.3
-
2
-
-
84869201485
-
Practical Bayesian optimization of machine learning algorithms
-
J. Snoek, H. Larochelle, and R. P. Adams. Practical Bayesian optimization of machine learning algorithms. In Proc. of NIPS'12, 2012.
-
(2012)
Proc. Of NIPS'12
-
-
Snoek, J.1
Larochelle, H.2
Adams, R.P.3
-
3
-
-
84949985138
-
Taking the human out of the loop: A review of Bayesian optimization
-
2015
-
B. Shahriari, K. Swersky, Z. Wang, R. Adams, and N. de Freitas. Taking the human out of the loop: A Review of Bayesian Optimization. Proc. of the IEEE, (1), 12/2015 2016.
-
(2016)
Proc. Of the IEEE
, Issue.1
, pp. 12
-
-
Shahriari, B.1
Swersky, K.2
Wang, Z.3
Adams, R.4
De Freitas, N.5
-
5
-
-
84949921865
-
Speeding up automatic hyperparameter optimization of deep neural networks by extrapolation of learning curves
-
Q. Yang and M. Wooldridge, editors
-
T. Domhan, J. T. Springenberg, and F. Hutter. Speeding up automatic hyperparameter optimization of deep neural networks by extrapolation of learning curves. In Q. Yang and M. Wooldridge, editors, Proc. of IJCAI'15, 2015.
-
(2015)
Proc. Of IJCAI'15
-
-
Domhan, T.1
Springenberg, J.T.2
Hutter, F.3
-
6
-
-
85049152514
-
Hyperband: Bandit-based configuration evaluation for hyperparameter optimization
-
L. Li, K. Jamieson, G. DeSalvo, A. Rostamizadeh, and A. Talwalkar. Hyperband: Bandit-based configuration evaluation for hyperparameter optimization. In Proc. of ICLR'17, 2017.
-
(2017)
Proc. Of ICLR'17
-
-
Li, L.1
Jamieson, K.2
DeSalvo, G.3
Rostamizadeh, A.4
Talwalkar, A.5
-
7
-
-
85083937790
-
Fast Bayesian optimization of machine learning hyperparameters on large datasets
-
A. Klein, S. Falkner, S. Bartels, P. Hennig, and F. Hutter. Fast Bayesian optimization of machine learning hyperparameters on large datasets. In Proc. of AISTATS'17, 2017.
-
(2017)
Proc. Of AISTATS'17
-
-
Klein, A.1
Falkner, S.2
Bartels, S.3
Hennig, P.4
Hutter, F.5
-
9
-
-
85007221118
-
Initializing Bayesian hyperparameter optimization via meta-learning
-
M. Feurer, T. Springenberg, and F. Hutter. Initializing Bayesian hyperparameter optimization via meta-learning. In Proc. of AAAI'15, 2015.
-
(2015)
Proc. Of AAAI'15
-
-
Feurer, M.1
Springenberg, T.2
Hutter, F.3
-
11
-
-
84856930049
-
Sequential model-based optimization for general algorithm configuration
-
F. Hutter, H. Hoos, and K. Leyton-Brown. Sequential model-based optimization for general algorithm configuration. In LION'11, 2011.
-
(2011)
LION'11
-
-
Hutter, F.1
Hoos, H.2
Leyton-Brown, K.3
-
12
-
-
84970022032
-
Scalable Bayesian optimization using deep neural networks
-
J. Snoek, O. Rippel, K. Swersky, R. Kiros, N. Satish, N. Sundaram, M. M. A. Patwary, Prabhat, and R. P. Adams. Scalable Bayesian optimization using deep neural networks. In Proc. of ICML'15, 2015.
-
(2015)
Proc. Of ICML'15
-
-
Snoek, J.1
Rippel, O.2
Swersky, K.3
Kiros, R.4
Satish, N.5
Sundaram, N.6
Patwary, M.M.A.7
Prabhat8
Adams, R.P.9
-
14
-
-
84969909658
-
Probabilistic backpropagation for scalable learning of Bayesian neural networks
-
J. Hernández-Lobato and R. Adams. Probabilistic backpropagation for scalable learning of Bayesian neural networks. In Proc. of ICML'15, 2015.
-
(2015)
Proc. Of ICML'15
-
-
Hernández-Lobato, J.1
Adams, R.2
-
15
-
-
80053452150
-
Bayesian learning via stochastic gradient Langevin dynamics
-
M. Welling and Y. Teh. Bayesian learning via stochastic gradient Langevin dynamics. In Proc. of ICML'11, 2011.
-
(2011)
Proc. Of ICML'11
-
-
Welling, M.1
Teh, Y.2
-
18
-
-
85070980911
-
Random forests
-
L. Breimann. Random forests. MLJ, 2001.
-
(2001)
MLJ
-
-
Breimann, L.1
-
19
-
-
84887848457
-
Algorithm runtime prediction: Methods and evaluation
-
F. Hutter, L. Xu, H. Hoos, and K. Leyton-Brown. Algorithm runtime prediction: Methods and evaluation. AIJ, 2014.
-
(2014)
AIJ
-
-
Hutter, F.1
Xu, L.2
Hoos, H.3
Leyton-Brown, K.4
-
21
-
-
15344347807
-
Gradient-based learning applied to document recognition
-
S. Haykin and B. Kosko, editors, IEEE Press
-
Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner. Gradient-based learning applied to document recognition. In S. Haykin and B. Kosko, editors, Intelligent Signal Processing. IEEE Press, 2001. URL http://www.iro.umontreal.ca/~lisa/pointeurs/lecun-01a.pdf.
-
(2001)
Intelligent Signal Processing
-
-
LeCun, Y.1
Bottou, L.2
Bengio, Y.3
Haffner, P.4
-
23
-
-
85040289534
-
Non-stochastic best arm identification and hyperparameter optimization
-
K. Jamieson and A. Talwalkar. Non-stochastic best arm identification and hyperparameter optimization. In Proc. of AISTATS'16, 2016.
-
(2016)
Proc. Of AISTATS'16
-
-
Jamieson, K.1
Talwalkar, A.2
|