Multimodal probabilistic reasoning for prediction and coordination problems in machine learning

Thesis

In this thesis we consider the role of multimodality in decision making and coordination problems in machine learning. We propose the use of a series of multimodal probabilistic methods, using extensions of (finite) mixture models to tackle challenges in time series forecasting, efficient uncertainty quantification in neural networks, adversarial models and multi-agent coordination.

In the first part of the thesis, we focus on the usage of multimodal uncertainty estimation for time...

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Authors: Zand, J

• Type of award: DPhil
• Level of award: Doctoral
• Awarding institution: University of Oxford
• Language: English
• Subjects: Machine learning