Laura Israel - Je ne regrette rien: Teaching Machine Learning Models Regret Avoidance

Описание: www.pydata.org

When providing ML-based optimization products with multiple independent components, we can easily loose the holistic view of the problem that we are trying to solve and might end up not making optimal decisions. The easy way out - switching from a modular approach to a single unified optimization model - is often not feasible though. In this talk, I will discuss and demonstrate the implementation of an alternative method that will allow ML models to consider potential behavior of other components in a complex system using regret-sensitive loss functions. By using a simple game-theoretical formalization of the system and quantifying regret (i.e., the experience of a sub-optimal decision when information about the best action comes available after the model was already called), we can widen the optimization scope of the model and increase the overall performance of complex decision processes.

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