Learning to optimize

5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings

Volumn , Issue , 2017, Pages

  Li, Ke ^a   Malik, Jitendra ^a  

^a UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

OPTIMIZATION;

ALGORITHM DESIGN; CONVERGENCE SPEED; FINAL OBJECTIVE; OPTIMIZATION ALGORITHMS; POLICY SEARCH;

REINFORCEMENT LEARNING;

EID: 85088230669     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (35)

1. Yaser S Abu-Mostafa. A method for learning from hints. In Advances in Neural Information Processing Systems, pp. 73-80, 1993.
2. Marcin Andrychowicz, Misha Denil, Sergio Gomez, Matthew W Hoffman, David Pfau, Tom Schaul, and Nando de Freitas. Learning to learn by gradient descent by gradient descent. arXiv preprint arXiv:1606.04474, 2016.
3. Jonathan Baxter, Rich Caruana, Tom Mitchell, Lorien Y Pratt, Daniel L Silver, and Sebastian Thrun. NIPS 1995 workshop on learning to learn: Knowledge consolidation and transfer in inductive systems. https://web.archive.org/web/20000618135816/http://www.cs.cmu.edu/afs/cs.cmu.edu/user/caruana/pub/transfer.html, 1995. Accessed: 2015-12-05.
4. Yoshua Bengio. Gradient-based optimization of hyperparameters. Neural computation, 12(8):1889-1900, 2000.
5. James Bergstra and Yoshua Bengio. Random search for hyper-parameter optimization. The Journal of Machine Learning Research, 13(1):281-305, 2012.
6. James S Bergstra, Rémi Bardenet, Yoshua Bengio, and Balázs Kégl. Algorithms for hyper-parameter optimization. In Advances in Neural Information Processing Systems, pp. 2546-2554, 2011.
7. Matthieu Bray, Esther Koller-meier, Pascal Müller, Luc Van Gool, and Nicol N Schraudolph. 3d hand tracking by rapid stochastic gradient descent using a skinning model. In 1st European Conference on Visual Media Production (CVMP). Citeseer, 2004.
8. Pavel Brazdil, Christophe Giraud Carrier, Carlos Soares, and Ricardo Vilalta. Metalearning: applications to data mining. Springer Science & Business Media, 2008.
9. Pavel B Brazdil, Carlos Soares, and Joaquim Pinto Da Costa. Ranking learning algorithms: Using ibl and meta-learning on accuracy and time results. Machine Learning, 50(3):251-277, 2003.
10. Eric Brochu, Vlad M Cora, and Nando De Freitas. A tutorial on bayesian optimization of expensive cost functions, with application to active user modeling and hierarchical reinforcement learning. arXiv preprint arXiv:1012.2599, 2010.
11. Nichael Lynn Cramer. A representation for the adaptive generation of simple sequential programs. In Proceedings of the First International Conference on Genetic Algorithms, pp. 183-187, 1985.
12. Christian Daniel, Jonathan Taylor, and Sebastian Nowozin. Learning step size controllers for robust neural network training. In Thirtieth AAAI Conference on Artificial Intelligence, 2016.
13. Justin Domke. Generic methods for optimization-based modeling. In AISTATS, volume 22, pp. 318-326, 2012.
14. Matthias Feurer, Jost Tobias Springenberg, and Frank Hutter. Initializing bayesian hyperparameter optimization via meta-learning. In AAAI, pp. 1128-1135, 2015.
15. Chelsea Finn, Xin Yu Tan, Yan Duan, Trevor Darrell, Sergey Levine, and Pieter Abbeel. Learning visual feature spaces for robotic manipulation with deep spatial autoencoders. arXiv preprint arXiv:1509.06113, 2015.
16. Jie Fu, Zichuan Lin, Miao Liu, Nicholas Leonard, Jiashi Feng, and Tat-Seng Chua. Deep q-networks for accelerating the training of deep neural networks. arXiv preprint arXiv:1606.01467, 2016.
17. Alex Graves, Greg Wayne, and Ivo Danihelka. Neural Turing machines. arXiv preprint arXiv:1410.5401, 2014.
18. Karol Gregor and Yann LeCun. Learning fast approximations of sparse coding. In Proceedings of the 27th International Conference on Machine Learning (ICML-10), pp. 399-406, 2010.
19. Weiqiao Han, Sergey Levine, and Pieter Abbeel. Learning compound multi-step controllers under unknown dynamics. In International Conference on Intelligent Robots and Systems, 2015.
20. Samantha Hansen. Using deep q-learning to control optimization hyperparameters. arXiv preprint arXiv:1602.04062, 2016.
21. Sepp Hochreiter, A Steven Younger, and Peter R Conwell. Learning to learn using gradient descent. In International Conference on Artificial Neural Networks, pp. 87-94. Springer, 2001.
22. Frank Hutter, Holger H Hoos, and Kevin Leyton-Brown. Sequential model-based optimization for general algorithm configuration. In Learning and Intelligent Optimization, pp. 507-523. Springer, 2011.
23. Sergey Levine and Pieter Abbeel. Learning neural network policies with guided policy search under unknown dynamics. In Advances in Neural Information Processing Systems, pp. 1071-1079, 2014.
24. Sergey Levine, Chelsea Finn, Trevor Darrell, and Pieter Abbeel. End-to-end training of deep visuo-motor policies. arXiv preprint arXiv:1504.00702, 2015a.
25. Sergey Levine, Nolan Wagener, and Pieter Abbeel. Learning contact-rich manipulation skills with guided policy search. arXiv preprint arXiv:1501.05611, 2015b.
26. Percy Liang, Michael I Jordan, and Dan Klein. Learning programs: A hierarchical Bayesian approach. In Proceedings of the 27th International Conference on Machine Learning (ICML-10), pp. 639-646, 2010.
27. Dougal Maclaurin, David Duvenaud, and Ryan P Adams. Gradient-based hyperparameter optimization through reversible learning. arXiv preprint arXiv:1502.03492, 2015.
28. Jonas Mockus, Vytautas Tiesis, and Antanas Zilinskas. The application of bayesian methods for seeking the extremum. Towards global optimization, 2(117-129):2, 1978.
29. Paul L Ruvolo, Ian Fasel, and Javier R Movellan. Optimization on a budget: A reinforcement learning approach. In Advances in Neural Information Processing Systems, pp. 1385-1392, 2009.
30. Jürgen Schmidhuber. Optimal ordered problem solver. Machine Learning, 54(3):211-254, 2004.
31. Jasper Snoek, Hugo Larochelle, and Ryan P Adams. Practical bayesian optimization of machine learning algorithms. In Advances in neural information processing systems, pp. 2951-2959, 2012.
32. Pablo Sprechmann, Roee Litman, Tal Ben Yakar, Alexander M Bronstein, and Guillermo Sapiro. Supervised sparse analysis and synthesis operators. In Advances in Neural Information Processing Systems, pp. 908-916, 2013.
33. Kevin Swersky, Jasper Snoek, and Ryan P Adams. Multi-task bayesian optimization. In Advances in neural information processing systems, pp. 2004-2012, 2013.
34. Sebastian Thrun and Lorien Pratt. Learning to learn. Springer Science & Business Media, 2012.
35. Ricardo Vilalta and Youssef Drissi. A perspective view and survey of meta-learning. Artificial Intelligence Review, 18(2):77-95, 2002.