In mathematical folklore, the "no free lunch" theorem (sometimes pluralized) of David Wolpert and William Macready appears in the 1997 "No Free Lunch… (More)

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2017

2017

- Daniel A. Ashlock, Diego Pérez-Liébana, Amanda Saunders
- 2017 IEEE Conference on Computational…
- 2017

Popular topics in current research within the games community are general game playing and general video game playing. Both of… (More)

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2011

Highly Cited

2011

- Daniel Kifer, Ashwin Machanavajjhala
- SIGMOD Conference
- 2011

Differential privacy is a powerful tool for providing privacy-preserving noisy query answers over statistical databases. It… (More)

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2009

2009

- Albert Y. S. Lam, Victor O. K. Li
- 2009 IEEE International Conference on Systems…
- 2009

The No-Free-Lunch (NFL) Theorem provides a fundamental limit governing all optimization/search algorithms and has successfully… (More)

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2009

2009

- Victor O. K. Li
- 2009

The No-Free-Lunch (NFL) Theorem provides a fundamental limit governing all optimization/search algorithms and has successfully… (More)

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2009

2009

- Jonathan E. Rowe, Michael D. Vose, Alden H. Wright
- Evolutionary Computation
- 2009

Abstract Since its inception, the "No Free Lunch" theorem (NFL) has concerned the application of symmetry results rather than the… (More)

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2008

2008

- Huan Xu, Constantine Caramanis, Shie Mannor
- IEEE Transactions on Pattern Analysis and Machine…
- 2008

We consider two desired properties of learning algorithms: sparsity and algorithmic stability. Both properties are believed to… (More)

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2005

2005

- LUNCH THEOREM, Darrell Whitley, Jean Paul Watson
- 2005

Note that “No Free Lunch” is often referred to simply as NFL within the heuristic search community (despite copyrights and… (More)

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2005

2005

We present a preliminary analysis of the fundamental viability of meta-learning, revisiting the No Free Lunch (NFL) theorem. The… (More)

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2004

Highly Cited

2004

- Christian Igel, Marc Toussaint
- J. Math. Model. Algorithms
- 2004

The sharpened No-Free-Lunch-theorem (NFL-theorem) states that, regardless of the performance measure, the performance of all… (More)

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2003

2003

- Christian Igel, Marc Toussaint
- ArXiv
- 2003

The sharpened No-Free-Lunch-theorem (NFL-theorem) states that the performance of all optimization algorithms averaged over any… (More)

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