TY - GEN
T1 - On Information-Theoretic Determination of Misspecified Rates of Convergence
AU - Weinberger, Nir
AU - Feder, Meir
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - We consider the problem of learning a model from given data samples in which the predictor's quality is measured by the log loss. We focus on the misspecified setting, in which the true model generating the data is chosen from a set different from the possible models that can be chosen by the learner. We establish minimax expected regret upper and lower bounds in terms of properly defined projected covering and packing entropies, and show their relation to M-projection geometric properties. We exemplify the bounds in a few settings.
AB - We consider the problem of learning a model from given data samples in which the predictor's quality is measured by the log loss. We focus on the misspecified setting, in which the true model generating the data is chosen from a set different from the possible models that can be chosen by the learner. We establish minimax expected regret upper and lower bounds in terms of properly defined projected covering and packing entropies, and show their relation to M-projection geometric properties. We exemplify the bounds in a few settings.
UR - http://www.scopus.com/inward/record.url?scp=85136319015&partnerID=8YFLogxK
U2 - 10.1109/ISIT50566.2022.9834743
DO - 10.1109/ISIT50566.2022.9834743
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AN - SCOPUS:85136319015
T3 - IEEE International Symposium on Information Theory - Proceedings
SP - 1695
EP - 1700
BT - 2022 IEEE International Symposium on Information Theory, ISIT 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2022 IEEE International Symposium on Information Theory, ISIT 2022
Y2 - 26 June 2022 through 1 July 2022
ER -