Difference between revisions of "Polynomial threshold"
m |
m |
||
| Line 9: | Line 9: | ||
* TODO: proposition 5.6 in O'Donnell says that functions with small noise sensitivity are close to low degree polynomial thresholds. | * TODO: proposition 5.6 in O'Donnell says that functions with small noise sensitivity are close to low degree polynomial thresholds. | ||
* TODO | * TODO | ||
| + | |||
| + | == Conjectures == | ||
| + | * Gotsman and Linial [13] conjectured, for example,that the average sensitivity of a degree-dpolynomial isO(d√n). (TODO: this is from Harsha at el paper, please cite properly, just here for keepsies) | ||
| + | |||
== References == | == References == | ||
<references/> | <references/> | ||
Revision as of 10:16, 29 October 2020
Definition
A function [math]f:\{-1,1\}^n \to \{-1,1\}[/math] is called a polynomial threshold function of degree at most [math]k[/math] if there exists a real polynomial [math]p(x)[/math] of degree at most [math]k[/math] such that [math]f(x) = \mathrm{sign}(p(x))[/math].
The special case [math]k = 1[/math] is of special interest; it is the linear threshold function.
Properties
- The average sensitivity of a degree-[math]k[/math] polynomial threshold function is bounded by [math]2^{O(d)}O(n^{1-1/(4k+6)})[/math]. [1]
- The noise sensitivity of a degree-[math]k[/math] polynomial threshold function, when the noise is of rate [math]\delta[/math], is at most [math]2^{O(d)}O(\delta^{1/(4k+6)})[/math]. [2]
- TODO: proposition 5.6 in O'Donnell says that functions with small noise sensitivity are close to low degree polynomial thresholds.
- TODO
Conjectures
- Gotsman and Linial [13] conjectured, for example,that the average sensitivity of a degree-dpolynomial isO(d√n). (TODO: this is from Harsha at el paper, please cite properly, just here for keepsies)
References
- ↑ Prahladh Harsha, Adam Klivans, Raghu Meka, Bounding the Sensitivity of Polynomial Threshold Functions, Theorem 1.6
- ↑ Prahladh Harsha, Adam Klivans, Raghu Meka, Bounding the Sensitivity of Polynomial Threshold Functions, Theorem 1.3
