mlsanityData to 14 Oct 2026

Distribution-Free Predictive Inference under Unknown Temporal Drift

arXiv 2406.06516 ↗First posted 10 Jun 2024stat.ME cs.LG stat.MLcited by 0

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Abstract (arXiv)

Distribution-free prediction sets play a pivotal role in uncertainty quantification for complex statistical models. Their validity hinges on reliable calibration data, which may not be readily available as real-world environments often undergo unknown changes over time. In this paper, we propose a strategy for choosing an adaptive window and use the data therein to construct prediction sets. The window is selected by optimizing an estimated bias-variance tradeoff. We provide sharp coverage guarantees for our method, showing its adaptivity to the underlying temporal drift. We also illustrate its efficacy through numerical experiments on synthetic and real data.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 34 of its 46 references that are in the map. First posted on arXiv 10 Jun 2024.

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34 of 46 references.

References · 34

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22 more
  1. 2023Conformal PID Control for Time Series Predictioncited by 88
  2. 2019Adaptive, Distribution-Free Prediction Intervals for Deep Networkscited by 56
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  11. 2020PAC Confidence Predictions for Deep Neural Network Classifierscited by 21
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  15. 2024Group-Weighted Conformal Predictioncited by 14
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  21. 2024Uncertainty Quantification With Multiple Sourcescited by 2
  22. 2024Single Trajectory Conformal Predictioncited by 1

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