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Invited Talk
Workshop: Workshop on Socially Responsible Machine Learning

Aaron Roth. Better Estimates of Prediction Uncertainty


How can we quantify the accuracy and uncertainty of predictions that we make in online decision problems? Standard approaches, like asking for calibrated predictions or giving prediction intervals using conformal methods give marginal guarantees --- i.e. they offer promises that are averages over the history of data points. Guarantees like this are unsatisfying when the data points correspond to people, and the predictions are used in important contexts --- like personalized medicine.

In this work, we study how to give stronger than marginal ("multivalid") guarantees for estimates of means, moments, and prediction intervals. Guarantees like this are valid not just as averaged over the entire population, but also as averaged over an enormous number of potentially intersecting demographic groups. We leverage techniques from game theory to give efficient algorithms promising these guarantees even in adversarial environments.