NDLI: Generalization bounds for non-stationary mixing processes

Content Provider	Springer Nature Link
Author	Kuznetsov, Vitaly Mohri, Mehryar
Copyright Year	2016
Abstract	This paper presents the first generalization bounds for time series prediction with a non-stationary mixing stochastic process. We prove Rademacher complexity learning bounds for both average-path generalization with non-stationary $$\beta $$ -mixing processes and path-dependent generalization with non-stationary $$\phi $$ -mixing processes. Our guarantees are expressed in terms of $$\beta $$ - or $$\phi $$ -mixing coefficients and a natural measure of discrepancy between training and target distributions. They admit as special cases previous Rademacher complexity bounds for non-i.i.d. stationary distributions, for independent but not identically distributed random variables, or for the i.i.d. case. We show that, using a new sub-sample selection technique we introduce, our bounds can be tightened under the natural assumption of asymptotically stationary stochastic processes. We also prove that fast learning rates can be achieved by extending existing local Rademacher complexity analyses to the non-i.i.d. setting. We conclude the paper by providing generalization bounds for learning with unbounded losses and non-i.i.d. data.
Starting Page	93
Ending Page	117
Page Count	25
File Format	PDF
ISSN	08856125
Journal	Machine Learning
Volume Number	106
Issue Number	1
e-ISSN	15730565
Language	English
Publisher	Springer US
Publisher Date	2016-10-03
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Generalization bounds Time series Mixing Non-stationary processes Markov processes Asymptotic stationarity Fast rates Local Rademacher complexity Unbounded loss Artificial Intelligence (incl. Robotics) Control, Robotics, Mechatronics Computing Methodologies Simulation and Modeling Language Translation and Linguistics
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Software

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