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Reasoning About Uncertainty using Markov Chains

Left: Performance of the “CLIP” model on accurately providing labels for images, dramatically outperforming previous work. Image from https://arxiv.org/pdf/2103.00020.pdf. Right: Summarizing a model’s performance by a single number is only one piece of information. Once this information is actually used to make a decision, we will also need to understand the different ways the model can fail. Image: own work.

Formal methods to tackle “Trial-and-Error” problems

The ability to deal with unseen objects in a zero-shot manner makes machine learning models very attractive for applications in robotics, .

While their accuracy in doing so is incredible compared with was conceivable just a few years ago, uncertainty is not only here to stay, but also requires a different treatment than customary in machine learning when used in decision making.

This article describes recent results on dealing with what we call “trial-and-error” tasks and explain how optimal decisions can be derived by modeling the system as a continuous-time Markov chain, aka Markov Jump Process.