TY - GEN
T1 - Self-Predicting Boolean Functions
AU - Weinberger, Nir
AU - Shayevitz, Ofer
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/8/15
Y1 - 2018/8/15
N2 - A Boolean function g is said to be an optimal predictor for another Boolean function f, if it minimizes the probability that f(X^{n})\neq g(Y^{n}) among all functions, where X^{n} is uniform over the Hamming cube and Y^{n} is obtained from X^{n} by independently flipping each coordinate with probability \delta. This paper is about self-predicting functions, which are those that coincide with their optimal predictor.
AB - A Boolean function g is said to be an optimal predictor for another Boolean function f, if it minimizes the probability that f(X^{n})\neq g(Y^{n}) among all functions, where X^{n} is uniform over the Hamming cube and Y^{n} is obtained from X^{n} by independently flipping each coordinate with probability \delta. This paper is about self-predicting functions, which are those that coincide with their optimal predictor.
UR - http://www.scopus.com/inward/record.url?scp=85052476866&partnerID=8YFLogxK
U2 - 10.1109/ISIT.2018.8437521
DO - 10.1109/ISIT.2018.8437521
M3 - ???researchoutput.researchoutputtypes.contributiontobookanthology.conference???
AN - SCOPUS:85052476866
SN - 9781538647806
T3 - IEEE International Symposium on Information Theory - Proceedings
SP - 276
EP - 280
BT - 2018 IEEE International Symposium on Information Theory, ISIT 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2018 IEEE International Symposium on Information Theory, ISIT 2018
Y2 - 17 June 2018 through 22 June 2018
ER -