Online Calibrated Regression for Adversarially Robust Forecasting
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Accurately estimating uncertainty is a crucial component of decision-making and forecasting in machine learning. However, existing uncertainty estimation methods developed for IID data may fail when these IID assumptions no longer hold. In this paper, we present a novel approach to uncertainty estimation that leverages the principles of online learning. Specifically, we define a task called online calibrated forecasting which seeks to extend existing online learning methods to handle predictive uncertainty while ensuring high accuracy. We introduce algorithms for this task that provide formal guarantees on the accuracy and calibration of probabilistic predictions even on adversarial input. We demonstrate the practical utility of our methods on several forecasting tasks, showing that our probabilistic predictions improve over natural baselines. Overall, our approach advances calibrated uncertainty estimation, and takes a step towards more robust and reliable decision-making and forecasting in risk-sensitive scenarios.
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