-
1
-
-
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
-
2
-
-
84919931099
-
Towards an empirical foundation for assessing Bayesian optimization of hyperparameters
-
K. Eggensperger, M. Feurer, F. Hutter, J. Bergstra, J. Snoek, H. Hoos, and K. Leyton-Brown. Towards an empirical foundation for assessing Bayesian optimization of hyperparameters. In BayesOpt'13, 2013.
-
(2013)
BayesOpt'13
-
-
Eggensperger, K.1
Feurer, M.2
Hutter, F.3
Bergstra, J.4
Snoek, J.5
Hoos, H.6
Leyton-Brown, K.7
-
3
-
-
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
-
5
-
-
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
-
7
-
-
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
-
9
-
-
0000561424
-
Efficient global optimization of expensive black box functions
-
D. Jones, M. Schonlau, and W. Welch. Efficient global optimization of expensive black box functions. JGO, 1998.
-
(1998)
JGO
-
-
Jones, D.1
Schonlau, M.2
Welch, W.3
-
10
-
-
77956501313
-
Gaussian process optimization in the bandit setting: No regret and experimental design
-
N. Srinivas, A. Krause, S. Kakade, and M. Seeger. Gaussian process optimization in the bandit setting: No regret and experimental design. In Proc. of ICML'10, 2010.
-
(2010)
Proc. of ICML'10
-
-
Srinivas, N.1
Krause, A.2
Kakade, S.3
Seeger, M.4
-
12
-
-
85019180185
-
Practical variational inference for neural networks
-
A. Graves. Practical variational inference for neural networks. In Proc. of ICML'11, 2011.
-
(2011)
Proc. of ICML'11
-
-
Graves, A.1
-
14
-
-
84969909658
-
Probabilistic backpropagation for scalable learning of Bayesian neural networks
-
J. M. 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.M.1
Adams, R.2
-
20
-
-
84965156531
-
A complete recipe for stochastic gradient MCMC
-
Y. Ma, T. Chen, and E.B. Fox. A complete recipe for stochastic gradient MCMC. In Proc. of NIPS'15, 2015.
-
(2015)
Proc. of NIPS'15
-
-
Ma, Y.1
Chen, T.2
Fox, E.B.3
-
21
-
-
85007196088
-
Preconditioned stochastic gradient langevin dynamics for deep neural networks
-
Chunyuan Li, Changyou Chen, David E. Carlson, and Lawrence Carin. Preconditioned stochastic gradient langevin dynamics for deep neural networks. In Proc. of AAAI'16, 2016.
-
(2016)
Proc. of AAAI'16
-
-
Li, C.1
Chen, C.2
Carlson, D.E.3
Carin, L.4
-
22
-
-
84986265678
-
Bridging the gap between stochastic gradient MCMC and stochastic optimization
-
Changyou Chen, David E. Carlson, Zhe Gan, Chunyuan Li, and Lawrence Carin. Bridging the gap between stochastic gradient MCMC and stochastic optimization. In Proc. of AISTATS, 2016.
-
(2016)
Proc. of AISTATS
-
-
Chen, C.1
Carlson, D.E.2
Gan, Z.3
Li, C.4
Carin, L.5
-
26
-
-
84944261791
-
OpenML: Networked science in machine learning
-
June
-
J. Vanschoren, J. van Rijn, B. Bischl, and L. Torgo. OpenML: Networked science in machine learning. SIGKDD Explor. Newsl., (2), June 2014.
-
(2014)
SIGKDD Explor. Newsl.
, Issue.2
-
-
Vanschoren, J.1
Van Rijn, J.2
Bischl, B.3
Torgo, L.4
-
27
-
-
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
-
29
-
-
85083953657
-
Continuous control with deep reinforcement learning
-
T. P. Lillicrap, J. J. Hunt, A. Pritzel, N. Heess, T. Erez, Y. Tassa, D. Silver, and D. Wierstra. Continuous control with deep reinforcement learning. In Proc. of ICLR, 2016.
-
(2016)
Proc. of ICLR
-
-
Lillicrap, T.P.1
Hunt, J.J.2
Pritzel, A.3
Heess, N.4
Erez, T.5
Tassa, Y.6
Silver, D.7
Wierstra, D.8
-
32
-
-
85019248308
-
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. CoRR, 2016.
-
(2016)
CoRR
-
-
Klein, A.1
Falkner, S.2
Bartels, S.3
Hennig, P.4
Hutter, F.5
|