We present the first protocol for distributed online prediction that aims to minimize online prediction loss and network communication at the same time. Applications include social content recommendation, algorithmic trading, and other scenarios where a configuration of local prediction models of high-frequency streams is used to provide a realtime service. For stationary data, the proposed protocol retains the asymptotic optimal regret of previous algorithms. At the same time, it allows to substantially reduce network communication, and, in contrast to previous approaches, it remains applicable when the data is non-stationary and shows rapid concept drift. The protocol is based on controlling the divergence of the local models in a decentralized way. Its beneficial properties are also confirmed empirically.
|Number of pages||6|
|Journal||CEUR Workshop Proceedings|
|State||Published - 2013|
|Event||1st International Workshop on Big Dynamic Distributed Data, BD3 2013 - Co-located with International Conference on Very Large Databases, VLDB 2013 - Riva del Garda, Italy|
Duration: 30 Aug 2013 → 30 Aug 2013
ASJC Scopus subject areas
- Computer Science (all)