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all right hi im anastasios im stephen and were going to be presenting to you a gentle introduction to conformal prediction and distribution free uncertainty quantification all right so its going to be like a chalk talk about how to generate rigorous finite sample confidence intervals that work for any model and any data set and cost very little computationally okay so the basic idea is that were going to be able to take some image or some input or something and then feed it into a neural network and then out of the neural network were not just going to get a point prediction were also going to get like a confidence interval a prediction interval in which the the desired output is guaranteed to be in the interval with high probability okay and the distinguishing features of the techniques that were going to talk about today is that they work in a black box manner you never have to analyze your model you never have to retrain your model um and weve described how to do this in a
