-
1
-
-
84868292661
-
Oracle inequalities for computationally budgeted model selection
-
A. Agarwal, J. Duchi, P. L. Bartlett, and C. Levrard. Oracle inequalities for computationally budgeted model selection. In COLT, 2011.
-
(2011)
COLT
-
-
Agarwal, A.1
Duchi, J.2
Bartlett, P.L.3
Levrard, C.4
-
2
-
-
84919949624
-
Least squares revisited: Scalable approaches for multi-class prediction
-
A. Agarwal, S. Kakade, N. Karampatziakis, L. Song, and G. Valiant. Least squares revisited: Scalable approaches for multi-class prediction. In ICML, 2014.
-
(2014)
ICML
-
-
Agarwal, A.1
Kakade, S.2
Karampatziakis, N.3
Song, L.4
Valiant, G.5
-
3
-
-
84857855190
-
Random search for hyper-parameter optimization
-
J. Bergstra and Y. Bengio. Random search for hyper-parameter optimization. In JMLR, 2012.
-
(2012)
JMLR
-
-
Bergstra, J.1
Bengio, Y.2
-
4
-
-
85162384813
-
Algorithms for hyper-parameter optimization
-
J. Bergstra et al. Algorithms for hyper-parameter optimization. In NIPS, 2011.
-
(2011)
NIPS
-
-
Bergstra, J.1
-
5
-
-
84949921865
-
Speeding up automatic hyperparameter optimization of deep neural networks by extrapolation of learning curves
-
T. Domhan, J. T. Springenberg, and F. Hutter. Speeding up automatic hyperparameter optimization of deep neural networks by extrapolation of learning curves. In IJCAI, 2015.
-
(2015)
IJCAI
-
-
Domhan, T.1
Springenberg, J.T.2
Hutter, F.3
-
6
-
-
84919931099
-
Towards an empirical foundation for assessing Bayesian optimization of hyperparameters
-
K. Eggensperger et al. Towards an empirical foundation for assessing bayesian optimization of hyperparameters. In NIPS Bayesian Optimization Workshop, 2013.
-
(2013)
NIPS Bayesian Optimization Workshop
-
-
Eggensperger, K.1
-
7
-
-
80053392476
-
Efficient multi-start strategies for local search algorithms
-
A. György and L. Kocsis. Efficient multi-start strategies for local search algorithms. JAIR, 41, 2011.
-
(2011)
JAIR
, vol.41
-
-
György, A.1
Kocsis, L.2
-
8
-
-
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 Proc. of LION-5, 2011.
-
(2011)
Proc. Of LION-5
-
-
Hutter, F.1
Hoos, H.2
Leyton-Brown, K.3
-
10
-
-
85010363885
-
Non-stochastic best arm identification and hyperparameter optimization
-
K. Jamieson and A. Talwalkar. Non-stochastic best arm identification and hyperparameter optimization. In AISTATS, 2015.
-
(2015)
AISTATS
-
-
Jamieson, K.1
Talwalkar, A.2
-
11
-
-
85034242641
-
-
arXiv preprint
-
A. Klein, S. Falkner, S. Bartels, P. Hennig, and F. Hutter. Fast bayesian optimization of machine learning hyperparameters on large datasets. arXiv preprint arXiv:1605.07079, 2016.
-
(2016)
Fast Bayesian Optimization of Machine Learning Hyperparameters on Large Datasets
-
-
Klein, A.1
Falkner, S.2
Bartels, S.3
Hennig, P.4
Hutter, F.5
-
12
-
-
77956002520
-
Learning multiple layers of features from tiny images
-
Department of Computer Science, Univsersity of Toronto
-
A. Krizhevsky. Learning multiple layers of features from tiny images. In Technical report, Department of Computer Science, Univsersity of Toronto, 2009.
-
(2009)
Technical Report
-
-
Krizhevsky, A.1
-
14
-
-
50249093806
-
An empirical evaluation of deep architectures on problems with many factors of variation
-
H. Larochelle et al. An empirical evaluation of deep architectures on problems with many factors of variation. In ICML, 2007.
-
(2007)
ICML
-
-
Larochelle, H.1
-
15
-
-
85020522534
-
-
L. Li, K. Jamieson, G. DeSalvo, A. Rostamizadeh, and A. Talwalkar. Hyperband: A novel bandit-based approach to hyperparameter optimization. arXiv:1603.06560, 2016.
-
(2016)
Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimization
-
-
Li, L.1
Jamieson, K.2
DeSalvo, G.3
Rostamizadeh, A.4
Talwalkar, A.5
-
16
-
-
0001923944
-
Hoeffding races: Accelerating model selection search for classification and function approximation
-
O. Maron and A. Moore. Hoeffding races: Accelerating model selection search for classification and function approximation. In NIPS, 1993.
-
(1993)
NIPS
-
-
Maron, O.1
Moore, A.2
-
18
-
-
77953218689
-
Random features for large-scale kernel machines
-
A. Rahimi and B. Recht. Random features for large-scale kernel machines. In NIPS, 2007.
-
(2007)
NIPS
-
-
Rahimi, A.1
Recht, B.2
-
20
-
-
56749117943
-
In defense of one-vs-all classification
-
R. Rifkin and A. Klautau. In defense of one-vs-all classification. JMLR, 2004.
-
(2004)
JMLR
-
-
Rifkin, R.1
Klautau, A.2
-
21
-
-
85007162574
-
Selecting near-optimal learners via incremental data allocation
-
A. Sabharwal, H. Samulowitz, and G. Tesauro. Selecting near-optimal learners via incremental data allocation. In AAAI, 2016.
-
(2016)
AAAI
-
-
Sabharwal, A.1
Samulowitz, H.2
Tesauro, G.3
-
22
-
-
84874575248
-
Convolutional neural networks applied to house numbers digit classification
-
P. Sermanet, S. Chintala, and Y. LeCun. Convolutional neural networks applied to house numbers digit classification. In ICPR, 2012.
-
(2012)
ICPR
-
-
Sermanet, P.1
Chintala, S.2
LeCun, Y.3
-
23
-
-
84869201485
-
Practical Bayesian optimization of machine learning algorithms
-
J. Snoek, H. Larochelle, and R. Adams. Practical bayesian optimization of machine learning algorithms. In NIPS, 2012.
-
(2012)
NIPS
-
-
Snoek, J.1
Larochelle, H.2
Adams, R.3
-
24
-
-
84970022032
-
Bayesian optimization using deep neural networks
-
J. Snoek et al. Bayesian optimization using deep neural networks. In ICML, 2015.
-
(2015)
ICML
-
-
Snoek, J.1
-
25
-
-
84958951297
-
Automating model search for large scale machine learning
-
E. Sparks, A. Talwalkar, D. Haas, M. J. Franklin, M. I. Jordan, and T. Kraska. Automating model search for large scale machine learning,. In Symposium on Cloud Computing, 2015.
-
(2015)
Symposium on Cloud Computing
-
-
Sparks, E.1
Talwalkar, A.2
Haas, D.3
Franklin, M.J.4
Jordan, M.I.5
Kraska, T.6
-
28
-
-
85018371540
-
Auto-weka: Combined selection and hyperparameter optimization of classification algorithms
-
C. Thornton et al. Auto-weka: Combined selection and hyperparameter optimization of classification algorithms. In KDD, 2013.
-
(2013)
KDD
-
-
Thornton, C.1
|