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Thesis

Conservative decision-making and inference in uncertain dynamical systems

Abstract:

The demand for automated decision making, learning and inference in uncertain, risk sensitive and dynamically changing situations presents a challenge: to design computational approaches that promise to be widely deployable and flexible to adapt on the one hand, while offering reliable guarantees on safety on the other. The tension between these desiderata has created a gap that, in spite of intensive research and contributions made from a wide range of communities, remains to be filled. T...

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Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Research group:
MLRG
Oxford college:
St Edmund Hall
Role:
Author

Contributors

Role:
Supervisor
Role:
Supervisor
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Funding agency for:
Calliess, J
Grant:
EPSRC EP/I011587
Publication date:
2014
Type of award:
DPhil
Level of award:
Doctoral
Awarding institution:
Oxford University, UK

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