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Tag: shifts

  • MIRRAMS: Towards Training Models Robust to Missingness Distribution Shifts

    MIRRAMS: Towards Training Models Robust to Missingness Distribution Shifts arXiv:2507.08280v1 Announce Type: new Abstract: In real-world data analysis, missingness distributional shifts between training and test input datasets frequently occur, posing a significant challenge to achieving robust prediction performance. In this study, we propose a novel deep learning framework designed to address such shifts in missingness…

  • CP$^2$: Leveraging Geometry for Conformal Prediction via Canonicalization

    CP$^2$: Leveraging Geometry for Conformal Prediction via Canonicalization arXiv:2506.16189v1 Announce Type: new Abstract: We study the problem of conformal prediction (CP) under geometric data shifts, where data samples are susceptible to transformations such as rotations or flips. While CP endows prediction models with post-hoc uncertainty quantification and formal coverage guarantees, their practicality breaks under distribution…

  • Conformal Prediction under L’evy-Prokhorov Distribution Shifts: Robustness to Local and Global Perturbations

    Conformal Prediction under L’evy-Prokhorov Distribution Shifts: Robustness to Local and Global Perturbations arXiv:2502.14105v1 Announce Type: new Abstract: Conformal prediction provides a powerful framework for constructing prediction intervals with finite-sample guarantees, yet its robustness under distribution shifts remains a significant challenge. This paper addresses this limitation by modeling distribution shifts using L’evy-Prokhorov (LP) ambiguity sets, which…

  • Sequential Harmful Shift Detection Without Labels

    Sequential Harmful Shift Detection Without Labels arXiv:2412.12910v1 Announce Type: new Abstract: We introduce a novel approach for detecting distribution shifts that negatively impact the performance of machine learning models in continuous production environments, which requires no access to ground truth data labels. It builds upon the work of Podkopaev and Ramdas [2022], who address scenarios…