@inproceedings{dc9dd9d8a831453aa5adafbf5082c9c3,

title = "An Upper Bound for Aggregating Algorithm for Regression with Changing Dependencies",

abstract = "The paper presents a competitive prediction-style upper bound on the square loss of the Aggregating Algorithm for Regression with Changing Dependencies in the linear case. The algorithm is able to compete with a sequence of linear predictors provided the sum of squared Euclidean norms of differences of regression coefficient vectors grows at a sublinear rate.",

keywords = "on-line learning, prediction with expert advice, competitive prediction, linear regression",

author = "Yury Kalnishkan",

year = "2016",

doi = "10.1007/978-3-319-46379-7_16",

language = "English",

isbn = "978-3-319-46378-0",

volume = "9925",

series = "Lecture Notes in Computer Science",

publisher = "Springer International Publishing",

pages = "238--252",

booktitle = "Algorithmic Learning Theory",

}