TY - GEN
T1 - Dependence measures bounding the exploration bias for general measurements
AU - Jiao, Jiantao
AU - Han, Yanjun
AU - Weissman, Tsachy
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/8/9
Y1 - 2017/8/9
N2 - We propose a framework to analyze and quantify the bias in adaptive data analysis. It generalizes that proposed by Russo and Zou'15, applying to measurements whose moment generating function exists, measurements with a finite p-norm, and measurements in general Orlicz spaces. We introduce a new class of dependence measures which retain key properties of mutual information while more effectively quantifying the exploration bias for heavy tailed distributions. We provide examples of cases where our bounds are nearly tight in situations where the original framework of Russo and Zou'15 does not apply.
AB - We propose a framework to analyze and quantify the bias in adaptive data analysis. It generalizes that proposed by Russo and Zou'15, applying to measurements whose moment generating function exists, measurements with a finite p-norm, and measurements in general Orlicz spaces. We introduce a new class of dependence measures which retain key properties of mutual information while more effectively quantifying the exploration bias for heavy tailed distributions. We provide examples of cases where our bounds are nearly tight in situations where the original framework of Russo and Zou'15 does not apply.
UR - http://www.scopus.com/inward/record.url?scp=85034052706&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85034052706&partnerID=8YFLogxK
U2 - 10.1109/ISIT.2017.8006774
DO - 10.1109/ISIT.2017.8006774
M3 - Conference contribution
AN - SCOPUS:85034052706
T3 - IEEE International Symposium on Information Theory - Proceedings
SP - 1475
EP - 1479
BT - 2017 IEEE International Symposium on Information Theory, ISIT 2017
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2017 IEEE International Symposium on Information Theory, ISIT 2017
Y2 - 25 June 2017 through 30 June 2017
ER -