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The Tradeoffs of Large-Scale Learning
Abstract
This contribution develops a theoretical framework that takes into account the effect of approximate optimization on learning algorithms. The analysis shows distinct tradeoffs for the case of small-scale and large-scale learning problems. Small-scale learning problems are subject to the usual approximation–estimation tradeoff. Largescale learning problems are subject to a qualitatively different tradeoff involving the computational complexity of the underlying optimization algorithm in non-trivial ways. For instance, a mediocre optimization algorithms, stochastic gradient descent, is shown to perform very well on large-scale learning problems. 1
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Cites
Measuring the VC-Dimension of a Learning Machine
Vapnik, V., Levin, E., & Cun, Y. L. (1994) · Neural Computation · doi:10.1162/neco.1994.6.5.851 · Cited by 388
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Large-Scale Machine Learning with Stochastic Gradient Descent
Bottou, L. (2010) · Proceedings of COMPSTAT'2010 · doi:10.1007/978-3-7908-2604-3_16 · Cited by 5,795
Self-citationGeneralizing from a Few Examples: A Survey on Few-shot Learning
Wang, Y., Yao, Q., Kwok, J. T., & Ni, L. M. (2021) · ACM Computing Surveys · doi:10.1145/3386252 · Cited by 3,165
(2015) · Cited by 2,857
Online dictionary learning for sparse coding
Mairal, J., Bach, F., Ponce, J., & Sapiro, G. (2009) · Proceedings of the 26th Annual International Conference on Machine Learning · doi:10.1145/1553374.1553463 · Cited by 2,114
Practical Recommendations for Gradient-Based Training of Deep Architectures
Bengio, Y. (2012) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-35289-8_26 · Cited by 2,259
Metadata
- Authors
- Bottou, L., & Bousquet, O.
- Venue
- Optimization for Machine Learning
- Publisher
- The MIT Press
- Published
- 2011-09-30
- Pages
- 351-368
- OpenAlex
- W2113651538
- Cited by
- 1,128
- References
- 1
- FWCI
- 214.40
- Type
- book chapter
- Language
- en
- Open access
- closed
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- Bottou, L., & Bousquet, O. (2011). The Tradeoffs of Large-Scale Learning. Optimization for Machine Learning. https://doi.org/10.7551/mitpress/8996.003.0015
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