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A Stitch in Time - Autonomous Model Management via Reinforcement Learning.
Elad
Liebman, Eric Zavesky, and Peter Stone.
In Proceedings of the 17th
International Conference on Autonomous Agents and Multiagent Systems (AAMAS), July 2018.
Concept drift - a change, either sudden or gradual, in the underlying properties of data - is one of the most prevalent challenges to maintaining high-performing learned models over time in autonomous systems. In the face of concept drift, one can hope that the old model is sufficiently representative of the new data despite the concept drift, one can discard the old data and retrain a new model with (often limited) new data, or one can use transfer learning methods to combine the old data with the new to create an updated model. Which of these three options is chosen affects not only near-term decisions, but also future needs to transfer or retrain. In this paper, we thus model response to concept drift as a sequential decision making problem and formally frame it as a Markov Decision Process. Our reinforcement learning approach to the problem shows promising results on one synthetic and two real-world datasets.
@InProceedings{AAMAS2018-eladlieb,
author = {Elad Liebman and Eric Zavesky and Peter Stone},
title = {{A} {S}titch in {T}ime - {A}utonomous {M}odel {M}anagement via {R}einforcement {L}earning},
booktitle = {Proceedings of the 17th International Conference on Autonomous Agents and Multiagent Systems (AAMAS)},
location = {Stockholm, Sweden},
month = {July},
year = {2018},
abstract = {
Concept drift - a change, either sudden or gradual, in
the underlying properties of data - is one of the most
prevalent challenges to maintaining high-performing
learned models over time in autonomous systems. In the
face of concept drift, one can hope that the old model
is sufficiently representative of the new data despite
the concept drift, one can discard the old data and
retrain a new model with (often limited) new data, or
one can use transfer learning methods to combine the old
data with the new to create an updated model. Which of
these three options is chosen affects not only near-term
decisions, but also future needs to transfer or retrain.
In this paper, we thus model response to concept drift
as a sequential decision making problem and formally
frame it as a Markov Decision Process. Our
reinforcement learning approach to the problem shows
promising results on one synthetic and two real-world
datasets.
},
}
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