pith. sign in

arxiv: 2602.19790 · v2 · submitted 2026-02-23 · 💻 cs.LG · stat.ML

Drift Localization using Conformal Predictions

Fabian Hinder , Valerie Vaquet , Johannes Brinkrolf , Barbara Hammer This is my paper

Pith reviewed 2026-05-15 20:12 UTC · model grok-4.3

classification 💻 cs.LG stat.ML
keywords concept driftdrift localizationconformal predictiondistribution shiftmachine learning monitoringimage datasets
0
0 comments X

The pith

Conformal predictions can localize which samples are affected by concept drift even in high-dimensional low-signal settings.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper proposes that conformal predictions offer a fundamentally different route to drift localization than the local testing schemes commonly used today. Those local methods tend to break down when data dimensionality is high and the drift signal is weak, but conformal approaches instead use calibration data to compute nonconformity scores that flag affected samples. The authors demonstrate that this works on state-of-the-art image datasets, giving practitioners a concrete way to identify exactly which inputs have changed rather than only detecting that drift has occurred globally.

Core claim

A drift localization method based on conformal predictions identifies affected samples by examining their nonconformity scores relative to a calibration set, thereby avoiding the failure modes of local hypothesis testing in high-dimensional, low-signal regimes, as validated through experiments on modern image datasets.

What carries the argument

Conformal predictions that produce valid prediction sets by ranking new samples against a calibration set using a nonconformity measure.

If this is right

  • Drift monitoring can move from global detection to per-sample identification without requiring separate tests per dimension.
  • Systems can respond to drift by isolating and adapting only on the affected subset of incoming data.
  • The approach scales to complex visual tasks where local statistics become unreliable.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same nonconformity scoring could be reused for related tasks such as selective retraining or active learning under distribution shift.
  • Testing the method on sequential or tabular data streams would reveal whether the advantage holds outside image domains.

Load-bearing premise

Conformal predictions can reliably determine which samples are affected by drift in high-dimensional, low-signal regimes where local testing fails.

What would settle it

A controlled experiment on a high-dimensional image dataset with known subtle drift locations in which the conformal method fails to identify the affected samples more accurately than local baselines would falsify the central claim.

Figures

Figures reproduced from arXiv: 2602.19790 by Barbara Hammer, Fabian Hinder, Johannes Brinkrolf, Valerie Vaquet.

Figure 1
Figure 1. Figure 1: Effect of training-calibration/test-split on performance — median and [PITH_FULL_IMAGE:figures/full_fig_p004_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Effect of number of bootstraps on ROC-AUC. Figure shows aggrega [PITH_FULL_IMAGE:figures/full_fig_p006_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Experimental results. ROC-AUC (500 runs) for various drift localizes [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗
read the original abstract

Concept drift -- the change of the distribution over time -- poses significant challenges for learning systems and is of central interest for monitoring. Understanding drift is thus paramount, and drift localization -- determining which samples are affected by the drift -- is essential. While several approaches exist, most rely on local testing schemes, which tend to fail in high-dimensional, low-signal settings. In this work, we consider a fundamentally different approach based on conformal predictions. We discuss and show the shortcomings of common approaches and demonstrate the performance of our approach on state-of-the-art image datasets.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

1 major / 0 minor

Summary. The paper claims that local testing schemes for drift localization fail in high-dimensional low-signal regimes and proposes a fundamentally different approach based on conformal predictions, with performance demonstrated on state-of-the-art image datasets.

Significance. If the conformal method delivers valid localization despite distribution shift, it would address a practical gap in monitoring deployed ML systems by identifying affected samples more reliably than local tests.

major comments (1)
  1. [Abstract] Abstract: the central claim that conformal predictions enable reliable drift localization is load-bearing, yet the provided text gives no indication of a modified construction (e.g., adaptive calibration or split) that would restore marginal coverage once drift violates exchangeability between calibration and test points; standard conformal guarantees therefore do not apply directly.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for the careful reading and for identifying the need to clarify the abstract's central claim. We address the comment below and will revise the manuscript accordingly.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the central claim that conformal predictions enable reliable drift localization is load-bearing, yet the provided text gives no indication of a modified construction (e.g., adaptive calibration or split) that would restore marginal coverage once drift violates exchangeability between calibration and test points; standard conformal guarantees therefore do not apply directly.

    Authors: We agree that standard conformal prediction requires exchangeability between calibration and test points, which is violated under drift, so the usual marginal coverage guarantees do not apply. Our manuscript does not introduce a modified construction (such as adaptive calibration) to restore those guarantees. Instead, the proposed method uses conformal scores to localize drift by identifying nonconforming samples relative to the calibration distribution, with the approach shown to outperform local testing in high-dimensional low-signal regimes on image datasets. We will revise the abstract to make this distinction explicit and to avoid implying that standard coverage guarantees hold under drift. revision: yes

Circularity Check

0 steps flagged

No significant circularity; no derivations or self-referential reductions present

full rationale

The manuscript abstract and context describe a high-level proposal to apply conformal predictions for drift localization, contrasting it with local testing schemes that fail in high-dimensional regimes, and report empirical performance on image datasets. No equations, parameter-fitting steps, uniqueness theorems, or self-citations appear in the provided text. Consequently, no load-bearing claim reduces by construction to its own inputs, no ansatz is smuggled via prior work, and no prediction is statistically forced from a fitted subset. The derivation chain is therefore self-contained at the level of description, consistent with the most common honest finding for papers lacking explicit technical content.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only review yields no visible free parameters, axioms, or invented entities; ledger left empty.

pith-pipeline@v0.9.0 · 5381 in / 885 out tokens · 26653 ms · 2026-05-15T20:12:00.537647+00:00 · methodology

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Reference graph

Works this paper leans on

14 extracted references · 14 canonical work pages · 2 internal anchors

  1. [1]

    Fabian Hinder, Valerie Vaquet, Johannes Brinkrolf, , and Barbara Hammer. Real vs. virtual drift: Creating realistic stream learning benchmarks. In Proceedings of the Euro- pean Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN), Bruges, Belgium, 2026

    work page 2026

  2. [2]

    J. Lu, A. Liu, F. Dong, F. Gu, J. Gama, and G. Zhang. Learning under concept drift: A review. IEEE transactions on knowledge and data engineering , 2018

    work page 2018

  3. [3]

    Hinder, V

    F. Hinder, V. Vaquet, and B. Hammer. One or two things we know about concept drift— a survey on monitoring in evolving environments. part b: locating and explaining concept drift. Frontiers in Artificial Intelligence , 2024

    work page 2024

  4. [4]

    Hinder, V

    F. Hinder, V. Vaquet, J. Brinkrolf, and B. Hammer. Model-based explanations of concept drift. Neurocomputing, 2023

    work page 2023

  5. [5]

    Hinder, V

    F. Hinder, V. Vaquet, J. Brinkrolf, A. Artelt, and B. Hammer. Localization of concept drift: Identifying the drifting datapoints. In IJCNN, 2022

    work page 2022

  6. [6]

    T. Dasu, S. Krishnan, S. Venkatasubramanian, and K. Yi. An information-theoretic approach to detecting changes in multi-dimensional data streams. 2006

    work page 2006

  7. [7]

    A. Liu, Y. Song, G. Zhang, and J. Lu. Regional concept drift detection and density synchronized drift adaptation. In IJCAI, 2017

    work page 2017

  8. [8]

    A. Liu, J. Lu, and G. Zhang. Concept drift detection via equal intensity k-means space partitioning. IEEE transactions on cybernetics , 2020

    work page 2020

  9. [9]

    B. A. Ramos, C. L. Castro, T. A. Coelho, and P. P. Angelov. Unsupervised drift detection using quadtree spatial mapping. 2024

    work page 2024

  10. [10]

    Hinder, A

    F. Hinder, A. Artelt, and B. Hammer. Towards non-parametric drift detection via dy- namic adapting window independence drift detection (dawidd). In ICML, 2020

    work page 2020

  11. [11]

    H. Xiao, K. Rasul, and R. Vollgraf. Fashion-mnist: a novel image dataset for benchmark- ing machine learning algorithms. arXiv preprint arXiv:1708.07747 , 2017

    work page internal anchor Pith review Pith/arXiv arXiv 2017

  12. [12]

    Bitterwolf, M

    J. Bitterwolf, M. Müller, and M. Hein. In or out? fixing imagenet out-of-distribution detection evaluation. In ICML, 2023

    work page 2023

  13. [13]

    DINOv2: Learning Robust Visual Features without Supervision

    M. Oquab, T. Darcet, T. Moutakanni, H. Vo, M. Szafraniec, V. Khalidov, P. Fernandez, D. Haziza, F. Massa, A. El-Nouby, et al. Dinov2: Learning robust visual features without supervision. arXiv preprint arXiv:2304.07193 , 2023

    work page internal anchor Pith review Pith/arXiv arXiv 2023

  14. [14]

    J. Deng, W. Dong, R. Socher, L. Li, K. Li, and L. Fei-Fei. Imagenet: A large-scale hierarchical image database. In CVPR, 2009. 8

    work page 2009