TY - GEN
T1 - Another Look at Universal Individual Learning
AU - Fogel, Yaniv
AU - Feder, Meir
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - In recent papers we have proposed an individual setting for the batch learning problem and showed that it is solved by a known variant of the Normalized Maximum Likelihood (NML) which we termed pNML. In this paper we present a different possible definition for the batch learning problem in the individual setting and show that it is solved by another known variant of the normalized maximum likelihood, which we denote by pNML2. We further derive an exact expression of the pNML2 for the linear regression problem. We use this result, along with known results and new upper and lower bounds over the regret of the pNML2 learner, to compare between the two learners.
AB - In recent papers we have proposed an individual setting for the batch learning problem and showed that it is solved by a known variant of the Normalized Maximum Likelihood (NML) which we termed pNML. In this paper we present a different possible definition for the batch learning problem in the individual setting and show that it is solved by another known variant of the normalized maximum likelihood, which we denote by pNML2. We further derive an exact expression of the pNML2 for the linear regression problem. We use this result, along with known results and new upper and lower bounds over the regret of the pNML2 learner, to compare between the two learners.
UR - http://www.scopus.com/inward/record.url?scp=85136266476&partnerID=8YFLogxK
U2 - 10.1109/ISIT50566.2022.9834661
DO - 10.1109/ISIT50566.2022.9834661
M3 - פרסום בספר כנס
AN - SCOPUS:85136266476
T3 - IEEE International Symposium on Information Theory - Proceedings
SP - 1501
EP - 1505
BT - 2022 IEEE International Symposium on Information Theory, ISIT 2022
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 26 June 2022 through 1 July 2022
ER -