Label Shift Estimation With Incremental Prior Update

• URL: http://arxiv.org/abs/2604.01651v1
• Date: Thu, 02 Apr 2026 05:47:59 GMT
• Title: Label Shift Estimation With Incremental Prior Update
• Abstract summary: We propose a new approach for post-hoc label shift estimation.<n>The proposed method relies on a weaker notion of calibration compared to other methods.<n> Experiments on CIFAR-10 and MNIST show that the proposed method consistently outperforms the current state-of-the-art maximum likelihood-based methods.
• Score: 8.570631199351363
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: An assumption often made in supervised learning is that the training and testing sets have the same label distribution. However, in real-life scenarios, this assumption rarely holds. For example, medical diagnosis result distributions change over time and across locations; fraud detection models must adapt as patterns of fraudulent activity shift; the category distribution of social media posts changes based on trending topics and user demographics. In the task of label shift estimation, the goal is to estimate the changing label distribution $p_t(y)$ in the testing set, assuming the likelihood $p(x|y)$ does not change, implying no concept drift. In this paper, we propose a new approach for post-hoc label shift estimation, unlike previous methods that perform moment matching with confusion matrix estimated from a validation set or maximize the likelihood of the new data with an expectation-maximization algorithm. We aim to incrementally update the prior on each sample, adjusting each posterior for more accurate label shift estimation. The proposed method is based on intuitive assumptions on classifiers that are generally true for modern probabilistic classifiers. The proposed method relies on a weaker notion of calibration compared to other methods. As a post-hoc approach for label shift estimation, the proposed method is versatile and can be applied to any black-box probabilistic classifier. Experiments on CIFAR-10 and MNIST show that the proposed method consistently outperforms the current state-of-the-art maximum likelihood-based methods under different calibrations and varying intensities of label shift.

Related papers

• SimPro: A Simple Probabilistic Framework Towards Realistic Long-Tailed Semi-Supervised Learning [49.94607673097326]
  We propose a highly adaptable framework, designated as SimPro, which does not rely on any predefined assumptions about the distribution of unlabeled data. Our framework, grounded in a probabilistic model, innovatively refines the expectation-maximization algorithm. Our method showcases consistent state-of-the-art performance across diverse benchmarks and data distribution scenarios.
  arXiv  Detail & Related papers  (2024-02-21T03:39:04Z)
• Channel-Selective Normalization for Label-Shift Robust Test-Time Adaptation [16.657929958093824]
  Test-time adaptation is an approach to adjust models to a new data distribution during inference. Test-time batch normalization is a simple and popular method that achieved compelling performance on domain shift benchmarks. We propose to tackle this challenge by only selectively adapting channels in a deep network, minimizing drastic adaptation that is sensitive to label shifts.
  arXiv  Detail & Related papers  (2024-02-07T15:41:01Z)
• Estimating calibration error under label shift without labels [47.57286245320775]
  Existing CE estimators assume access to labels from the target domain, which are often unavailable in practice, i.e., when the model is deployed and used. This work proposes a novel CE estimator under label shift, which is characterized by changes in the marginal label distribution $p(Y)$ while keeping the conditional $p(X|Y)$ constant between the source and target distributions. Our contribution is an approach, which, by leveraging importance re-weighting of the labeled source distribution, provides consistent and unbiased CE estimation with respect to the shifted target distribution.
  arXiv  Detail & Related papers  (2023-12-14T01:18:51Z)
• Variational Classification [51.2541371924591]
  We derive a variational objective to train the model, analogous to the evidence lower bound (ELBO) used to train variational auto-encoders. Treating inputs to the softmax layer as samples of a latent variable, our abstracted perspective reveals a potential inconsistency. We induce a chosen latent distribution, instead of the implicit assumption found in a standard softmax layer.
  arXiv  Detail & Related papers  (2023-05-17T17:47:19Z)
• Dist-PU: Positive-Unlabeled Learning from a Label Distribution Perspective [89.5370481649529]
  We propose a label distribution perspective for PU learning in this paper. Motivated by this, we propose to pursue the label distribution consistency between predicted and ground-truth label distributions. Experiments on three benchmark datasets validate the effectiveness of the proposed method.
  arXiv  Detail & Related papers  (2022-12-06T07:38:29Z)
• Beyond Invariance: Test-Time Label-Shift Adaptation for Distributions with "Spurious" Correlations [44.99833362998488]
  Changes in the data distribution at test time can have deleterious effects on the performance of predictive models. We propose a test-time label shift correction that adapts to changes in the joint distribution $p(y, z)$ using EM applied to unlabeled samples.
  arXiv  Detail & Related papers  (2022-11-28T18:52:33Z)
• Test-time Recalibration of Conformal Predictors Under Distribution Shift Based on Unlabeled Examples [30.61588337557343]
  Conformal predictors provide uncertainty estimates by computing a set of classes with a user-specified probability. We propose a method that provides excellent uncertainty estimates under natural distribution shifts.
  arXiv  Detail & Related papers  (2022-10-09T04:46:00Z)
• Domain Adaptation under Open Set Label Shift [39.424134505152544]
  We introduce the problem of domain adaptation under Open Set Label Shift (OSLS) OSLS subsumes domain adaptation under label shift and Positive-Unlabeled (PU) learning. We propose practical methods for both tasks that leverage black-box predictors.
  arXiv  Detail & Related papers  (2022-07-26T17:09:48Z)
• A unified framework for dataset shift diagnostics [2.449909275410288]
  Supervised learning techniques typically assume training data originates from the target population. Yet, dataset shift frequently arises, which, if not adequately taken into account, may decrease the performance of their predictors. We propose a novel and flexible framework called DetectShift that quantifies and tests for multiple dataset shifts.
  arXiv  Detail & Related papers  (2022-05-17T13:34:45Z)
• Deep learning model solves change point detection for multiple change types [69.77452691994712]
  A change points detection aims to catch an abrupt disorder in data distribution. We propose an approach that works in the multiple-distributions scenario.
  arXiv  Detail & Related papers  (2022-04-15T09:44:21Z)
• Leveraging Unlabeled Data to Predict Out-of-Distribution Performance [63.740181251997306]
  Real-world machine learning deployments are characterized by mismatches between the source (training) and target (test) distributions. In this work, we investigate methods for predicting the target domain accuracy using only labeled source data and unlabeled target data. We propose Average Thresholded Confidence (ATC), a practical method that learns a threshold on the model's confidence, predicting accuracy as the fraction of unlabeled examples.
  arXiv  Detail & Related papers  (2022-01-11T23:01:12Z)
• Coping with Label Shift via Distributionally Robust Optimisation [72.80971421083937]
  We propose a model that minimises an objective based on distributionally robust optimisation (DRO) We then design and analyse a gradient descent-proximal mirror ascent algorithm tailored for large-scale problems to optimise the proposed objective.
  arXiv  Detail & Related papers  (2020-10-23T08:33:04Z)
• Sequential changepoint detection in classification data under label shift [1.6752182911522522]
  We consider the problem of detecting such a change in distribution in sequentially-observed, unlabeled classification data. In simulations, we show that our method outperforms other detection procedures in this label shift setting.
  arXiv  Detail & Related papers  (2020-09-18T02:26:46Z)
• Bayesian Semi-supervised Multi-category Classification under Nonparanormality [2.307581190124002]
  Semi-supervised learning is a model training method that uses both labeled and unlabeled data. This paper proposes a fully Bayes semi-supervised learning algorithm that can be applied to any multi-category classification problem.
  arXiv  Detail & Related papers  (2020-01-11T21:31:25Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.