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Creators/Authors contains: "Zhou, Yanfei"

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  1. ; (, NeurIPS Proceedings)
    Accepted Manuscript Full Text Available
  2. ; ; (, PMLR)
    This paper presents a new conformal method for generating simultaneous forecasting bands guaranteed to cover the entire path of a new random trajectory with sufficiently high probability. Prompted by the need for dependable uncertainty estimates in motion planning applications where the behavior of diverse objects may be more or less unpredictable, we blend different techniques from online conformal prediction of single and multiple time series, as well as ideas for addressing heteroscedasticity in regression. This solution is both principled, providing precise finite-sample guarantees, and effective, often leading to more informative predictions than prior methods. 
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    Accepted Manuscript Full Text Available
  3. ; ; (, Proceedings of the 40 th International Conference on Machine Learning)
    Early stopping based on hold-out data is a popular regularization technique designed to mitigate overfitting and increase the predictive accuracy of neural networks. Models trained with early stopping often provide relatively accurate predictions,but they generally still lack precise statistical guarantees unless they are further calibrated using independent hold-out data. This paper addresses the above limitation with conformalized early stopping: a novel method that combines early stopping with conformal calibration while efficiently recycling the same hold-out data. This leads to models that are both accurate and able to provide exact predictive inferences without multiple data splits nor overly conservative adjustments. Practical implementations are developed for different learning tasks—outlier detection, multi-class classification, regression—and their competitive performance is demonstrated on real data. 
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    Accepted Manuscript Full Text Available