Peter Stone's Selected Publications

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A Stitch in Time - Autonomous Model Management via Reinforcement Learning

Elad Liebman, Eric Zavesky, and Peter Stone. A Stitch in Time - Autonomous Model Management via Reinforcement Learning. In Proceedings of the 17th International Conference on Autonomous Agents and Multiagent Systems (AAMAS), July 2018.

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Abstract

Concept drift - a change, either sudden or gradual, in the underlyingproperties of data - is one of the most prevalent challenges tomaintaining high-performing learned models over time in autonomoussystems. In the face of concept drift, one can hope that the old modelis sufficiently representative of the new data despite the conceptdrift, one can discard the old data and retrain a new model with (oftenlimited) new data, or one can use transfer learning methods to combinethe old data with the new to create an updated model. Which ofthese three options is chosen affects not only near-term decisions, butalso future needs to transfer or retrain. In this paper, we thus modelresponse to concept drift as a sequential decision making problem andformally frame it as a Markov Decision Process. Our reinforcementlearning approach to the problem shows promising results on onesynthetic and two real-world datasets.

BibTeX Entry

@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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