-
1
-
-
84897563832
-
Thompson sampling for contextual bandits with linear payoffs
-
S. Agrawal and N. Goyal. Thompson sampling for contextual bandits with linear payoffs. In ICML, 2013.
-
(2013)
ICML
-
-
Agrawal, S.1
Goyal, N.2
-
2
-
-
77956649096
-
A survey of cross-validation procedures for model selection
-
S. Arlot and A. Celisse. A survey of cross-validation procedures for model selection. Statistics Surveys, 4:40-79, 2010.
-
(2010)
Statistics Surveys
, vol.4
, pp. 40-79
-
-
Arlot, S.1
Celisse, A.2
-
3
-
-
84864970677
-
Best arm identification in multi-armed bandits
-
J.-Y. Audibert, S. Bubeck, and R. Munos. Best arm identification in multi-armed bandits. In CoLT, 2010.
-
(2010)
CoLT
-
-
Audibert, J.-Y.1
Bubeck, S.2
Munos, R.3
-
4
-
-
0036568025
-
Finite-time analysis of the multiarmed bandit problem
-
P. Auer, N. Cesa-Bianchi, and P. Fischer. Finite-time analysis of the multiarmed bandit problem. Machine Learning, 47(2):235-256, 2002.
-
(2002)
Machine Learning
, vol.47
, Issue.2
, pp. 235-256
-
-
Auer, P.1
Cesa-Bianchi, N.2
Fischer, P.3
-
5
-
-
85162387635
-
Budgeted optimization with concurrent stochastic-duration experiments
-
J. Azimi, A. Fern, and X. Fern. Budgeted optimization with concurrent stochastic-duration experiments. In NIPS, pages 1098-1106, 2011.
-
(2011)
NIPS
, pp. 1098-1106
-
-
Azimi, J.1
Fern, A.2
Fern, X.3
-
6
-
-
85162384813
-
Algorithms for hyper-parameter optimization
-
J. Bergstra, R. Bardenet, Y. Bengio, and B. Kégl. Algorithms for hyper-parameter optimization. In NIPS, pages 2546-2554, 2011.
-
(2011)
NIPS
, pp. 2546-2554
-
-
Bergstra, J.1
Bardenet, R.2
Bengio, Y.3
Kégl, B.4
-
7
-
-
85043560166
-
Active preference learning with discrete choice data
-
E. Brochu, N. de Freitas, and A. Ghosh. Active preference learning with discrete choice data. In NIPS, pages 409-416, 2007.
-
(2007)
NIPS
, pp. 409-416
-
-
Brochu, E.1
De Freitas, N.2
Ghosh, A.3
-
11
-
-
0000354976
-
A comparative study of ordinary cross-validation, v-fold cross-validation and the repeated learning-testing methods
-
P. Burman. A comparative study of ordinary cross-validation, v-fold cross-validation and the repeated learning-testing methods. Biometrika, 76(3):pp. 503-514, 1989.
-
(1989)
Biometrika
, vol.76
, Issue.3
, pp. 503-514
-
-
Burman, P.1
-
13
-
-
84875736450
-
An empirical evaluation of thompson sampling
-
O. Chapelle and L. Li. An empirical evaluation of Thompson sampling. In NIPS, 2012.
-
(2012)
NIPS
-
-
Chapelle, O.1
Li, L.2
-
14
-
-
84867124523
-
Exponential regret bounds for Gaussian process bandits with deterministic observations
-
N. de Freitas, A. Smola, and M. Zoghi. Exponential Regret Bounds for Gaussian Process Bandits with Deterministic Observations. In ICML, 2012.
-
(2012)
ICML
-
-
De Freitas, N.1
Smola, A.2
Zoghi, M.3
-
16
-
-
84877730309
-
Best arm identification: A unified approach to fixed budget and fixed confidence
-
V. Gabillon, M. Ghavamzadeh, and A. Lazaric. Best arm identification: A unified approach to fixed budget and fixed confidence. In NIPS, 2012.
-
(2012)
NIPS
-
-
Gabillon, V.1
Ghavamzadeh, M.2
Lazaric, A.3
-
17
-
-
84873695090
-
Self-avoiding random dynamics on integer complex systems
-
F. Hamze, Z. Wang, and N. de Freitas. Self-avoiding random dynamics on integer complex systems. ACM Transactions on Modelling and Computer Simulation, 23(1): 9:1-9:25, 2013.
-
(2013)
ACM Transactions on Modelling and Computer Simulation
, vol.23
, Issue.1
, pp. 901-925
-
-
Hamze, F.1
Wang, Z.2
De Freitas, N.3
-
18
-
-
84864947871
-
Entropy search for information-efficient global optimization
-
P. Hennig and C. Schuler. Entropy search for information-efficient global optimization. JMLR, 13:1809-1837, 2012.
-
(2012)
JMLR
, vol.13
, pp. 1809-1837
-
-
Hennig, P.1
Schuler, C.2
-
19
-
-
80053160717
-
Portfolio allocation for Bayesian optimization
-
M. W. Hoffman, E. Brochu, and N. de Freitas. Portfolio allocation for Bayesian optimization. In UAI, pages 327-336, 2011.
-
(2011)
UAI
, pp. 327-336
-
-
Hoffman, M.W.1
Brochu, E.2
De Freitas, N.3
-
20
-
-
84868554032
-
Sequential model-based optimization for general algorithm configuration
-
F. Hutter, H. H. Hoos, and K. Leyton-Brown. Sequential model-based optimization for general algorithm configuration. In Proceedings of LION-5, page 507-523, 2011.
-
(2011)
Proceedings of LION-5
-
-
Hutter, F.1
Hoos, H.H.2
Leyton-Brown, K.3
-
21
-
-
0035577808
-
A taxonomy of global optimization methods based on response surfaces
-
D. Jones. A taxonomy of global optimization methods based on response surfaces. J. of Global Optimization, 21(4):345-383, 2001.
-
(2001)
J. of Global Optimization
, vol.21
, Issue.4
, pp. 345-383
-
-
Jones, D.1
-
23
-
-
84867888879
-
On Bayesian upper conf bounds for bandit problems
-
E. Kaufmann, O. Cappé, and A. Garivier. On Bayesian upper conf. bounds for bandit problems. In AIStats, 2012a.
-
(2012)
AIStats
-
-
Kaufmann, E.1
Cappé, O.2
Garivier, A.3
-
25
-
-
57849115716
-
Controlled experiments on the web: Survey and practical guide
-
R. Kohavi, R. Longbotham, D. Sommerfield, and R. Henne. Controlled experiments on the web: survey and practical guide. Data Mining and Knowledge Discovery, 18:140-181, 2009.
-
(2009)
Data Mining and Knowledge Discovery
, vol.18
, pp. 140-181
-
-
Kohavi, R.1
Longbotham, R.2
Sommerfield, D.3
Henne, R.4
-
27
-
-
68749108525
-
Inference and learning for active sensing, experimental design and control
-
H. Araujo, A. Mendonca, A. Pinho, and M. Torres, editors Springer Berlin Heidelberg
-
H. Kueck, M. Hoffman, A. Doucet, and N. de Freitas. Inference and learning for active sensing, experimental design and control. In H. Araujo, A. Mendonca, A. Pinho, and M. Torres, editors, Pattern Recognition and Image Analysis, Volume 5524, pages 1-10. Springer Berlin Heidelberg, 2009.
-
(2009)
Pattern Recognition and Image Analysis
, vol.5524
, pp. 1-10
-
-
Kueck, H.1
Hoffman, M.2
Doucet, A.3
De Freitas, N.4
-
28
-
-
84863783543
-
An experimental methodology for response surface optimization methods
-
D. J. Lizotte, R. Greiner, and D. Schuurmans. An experimental methodology for response surface optimization methods. Journal of Global Optimization, 53(4):699-736, 2012.
-
(2012)
Journal of Global Optimization
, vol.53
, Issue.4
, pp. 699-736
-
-
Lizotte, D.J.1
Greiner, R.2
Schuurmans, D.3
-
29
-
-
84954528329
-
Adaptive MCMC with Bayesian optimization
-
N. Mahendran, Z. Wang, F. Hamze, and N. de Freitas. Adaptive MCMC with Bayesian optimization. Journal of Machine Learning Research - Proceedings Track, 22: 751-760, 2012.
-
(2012)
Journal of Machine Learning Research - Proceedings Track
, vol.22
, pp. 751-760
-
-
Mahendran, N.1
Wang, Z.2
Hamze, F.3
De Freitas, N.4
-
30
-
-
0001923944
-
Hoeffding races: Accelerating model selection search for classification and function approximation
-
O. Maron and A. W. Moore. Hoeffding races: Accelerating model selection search for classification and function approximation. In NIPS, pages 59-66, 1994.
-
(1994)
NIPS
, pp. 59-66
-
-
Maron, O.1
Moore, A.W.2
-
32
-
-
84867137347
-
The Bayesian approach to global optimization
-
Springer
-
J. Močkus. The Bayesian approach to global optimization. In Systems Modeling and Optimization, Volume 38, pages 473-481. Springer, 1982.
-
(1982)
Systems Modeling and Optimization
, vol.38
, pp. 473-481
-
-
Močkus, J.1
-
33
-
-
85162504694
-
Optimistic optimization of a deterministic function without the knowledge of its smoothness
-
R. Munos. Optimistic optimization of a deterministic function without the knowledge of its smoothness. In NIPS, pages 783-791, 2011.
-
(2011)
NIPS
, pp. 783-791
-
-
Munos, R.1
-
37
-
-
84869201485
-
Practical Bayesian optimization of machine learning algorithms
-
J. Snoek, H. Larochelle, and R. Adams. Practical Bayesian optimization of machine learning algorithms. In NIPS, pages 2960-2968, 2012.
-
(2012)
NIPS
, pp. 2960-2968
-
-
Snoek, J.1
Larochelle, H.2
Adams, R.3
-
38
-
-
77956501313
-
Gaussian process optimization in the bandit setting: No regret and experimental design
-
N. Srinivas, A. Krause, S. M. Kakade, and M. Seeger. Gaussian process optimization in the bandit setting: No regret and experimental design. In ICML, 2010.
-
(2010)
ICML
-
-
Srinivas, N.1
Krause, A.2
Kakade, S.M.3
Seeger, M.4
-
40
-
-
85018371540
-
Auto-WEKA: Combined selection and hyperparameter optimization of classification algorithms
-
C. Thornton, F. Hutter, H. H. Hoos, and K. Leyton-Brown. Auto-WEKA: Combined selection and hyperparameter optimization of classification algorithms. In KDD, pages 847-855, 2013.
-
(2013)
KDD
, pp. 847-855
-
-
Thornton, C.1
Hutter, F.2
Hoos, H.H.3
Leyton-Brown, K.4
-
41
-
-
84897517236
-
Stochastic simultaneous optimistic optimization
-
M. Valko, A. Carpentier, and R. Munos. Stochastic simultaneous optimistic optimization. In ICML, 2013.
-
(2013)
ICML
-
-
Valko, M.1
Carpentier, A.2
Munos, R.3
-
42
-
-
67650938640
-
An informational approach to the global optimization of expensive-to-evaluate functions
-
J. Villemonteix, E. Vazquez, and E. Walter. An informational approach to the global optimization of expensive-to-evaluate functions. Journal of Global Optimization, 44(4):509-534, 2009.
-
(2009)
Journal of Global Optimization
, vol.44
, Issue.4
, pp. 509-534
-
-
Villemonteix, J.1
Vazquez, E.2
Walter, E.3
-
44
-
-
84890958552
-
-
In IJCAI
-
Z. Wang, M. Zoghi, D. Matheson, F. Hutter, and N. de Freitas. Bayesian optimization in high dimensions via random embeddings. In IJCAI, 2013b.
-
(2013)
Bayesian Optimization in High Dimensions Via Random Embeddings
-
-
Wang, Z.1
Zoghi, M.2
Matheson, D.3
Hutter, F.4
De Freitas, N.5
|