Conformal Prediction Under Generalized Covariate Shift with Posterior Drift

• URL: http://arxiv.org/abs/2502.17744v1
• Date: Tue, 25 Feb 2025 00:46:43 GMT
• Title: Conformal Prediction Under Generalized Covariate Shift with Posterior Drift
• Authors: Baozhen Wang, Xingye Qiao,
• Abstract summary: A transfer learning approach aims to leverage knowledge from a related source domain to improve the learning performance in the target domain.<n>In this article, we study a particular type of classification problem, called a conformal prediction, under a new distributional assumption for transfer learning.
• Score: 3.7307776333361122
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In many real applications of statistical learning, collecting sufficiently many training data is often expensive, time-consuming, or even unrealistic. In this case, a transfer learning approach, which aims to leverage knowledge from a related source domain to improve the learning performance in the target domain, is more beneficial. There have been many transfer learning methods developed under various distributional assumptions. In this article, we study a particular type of classification problem, called conformal prediction, under a new distributional assumption for transfer learning. Classifiers under the conformal prediction framework predict a set of plausible labels instead of one single label for each data instance, affording a more cautious and safer decision. We consider a generalization of the \textit{covariate shift with posterior drift} setting for transfer learning. Under this setting, we propose a weighted conformal classifier that leverages both the source and target samples, with a coverage guarantee in the target domain. Theoretical studies demonstrate favorable asymptotic properties. Numerical studies further illustrate the usefulness of the proposed method.

Related papers

• Harnessing the Power of Vicinity-Informed Analysis for Classification under Covariate Shift [9.530897053573186]
  This paper introduces a novel dissimilarity measure that utilizes vicinity information, i.e., the local structure of data points.<n>We characterize the excess error using the proposed measure and demonstrate faster or competitive convergence rates compared to previous techniques.
  arXiv  Detail & Related papers  (2024-05-27T07:55:27Z)
• 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)
• 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)
• An Information-theoretical Approach to Semi-supervised Learning under Covariate-shift [24.061858945664856]
  A common assumption in semi-supervised learning is that the labeled, unlabeled, and test data are drawn from the same distribution. We propose an approach for semi-supervised learning algorithms that is capable of addressing this issue. Our framework also recovers some popular methods, including entropy minimization and pseudo-labeling.
  arXiv  Detail & Related papers  (2022-02-24T14:25:14Z)
• Domain Conditional Predictors for Domain Adaptation [3.951376400628575]
  We consider a conditional modeling approach in which predictions, in addition to being dependent on the input data, use information relative to the underlying data-generating distribution. We argue that such an approach is more generally applicable than current domain adaptation methods.
  arXiv  Detail & Related papers  (2021-06-25T22:15:54Z)
• An Effective Baseline for Robustness to Distributional Shift [5.627346969563955]
  Refraining from confidently predicting when faced with categories of inputs different from those seen during training is an important requirement for the safe deployment of deep learning systems. We present a simple, but highly effective approach to deal with out-of-distribution detection that uses the principle of abstention.
  arXiv  Detail & Related papers  (2021-05-15T00:46:11Z)
• Instance Level Affinity-Based Transfer for Unsupervised Domain Adaptation [74.71931918541748]
  We propose an instance affinity based criterion for source to target transfer during adaptation, called ILA-DA. We first propose a reliable and efficient method to extract similar and dissimilar samples across source and target, and utilize a multi-sample contrastive loss to drive the domain alignment process. We verify the effectiveness of ILA-DA by observing consistent improvements in accuracy over popular domain adaptation approaches on a variety of benchmark datasets.
  arXiv  Detail & Related papers  (2021-04-03T01:33:14Z)
• Source Data-absent Unsupervised Domain Adaptation through Hypothesis Transfer and Labeling Transfer [137.36099660616975]
  Unsupervised adaptation adaptation (UDA) aims to transfer knowledge from a related but different well-labeled source domain to a new unlabeled target domain. Most existing UDA methods require access to the source data, and thus are not applicable when the data are confidential and not shareable due to privacy concerns. This paper aims to tackle a realistic setting with only a classification model available trained over, instead of accessing to the source data.
  arXiv  Detail & Related papers  (2020-12-14T07:28:50Z)
• Learning Invariant Representations and Risks for Semi-supervised Domain Adaptation [109.73983088432364]
  We propose the first method that aims to simultaneously learn invariant representations and risks under the setting of semi-supervised domain adaptation (Semi-DA) We introduce the LIRR algorithm for jointly textbfLearning textbfInvariant textbfRepresentations and textbfRisks.
  arXiv  Detail & Related papers  (2020-10-09T15:42:35Z)
• Rectifying Pseudo Label Learning via Uncertainty Estimation for Domain Adaptive Semantic Segmentation [49.295165476818866]
  This paper focuses on the unsupervised domain adaptation of transferring the knowledge from the source domain to the target domain in the context of semantic segmentation. Existing approaches usually regard the pseudo label as the ground truth to fully exploit the unlabeled target-domain data. This paper proposes to explicitly estimate the prediction uncertainty during training to rectify the pseudo label learning.
  arXiv  Detail & Related papers  (2020-03-08T12:37:19Z)
• A Balanced and Uncertainty-aware Approach for Partial Domain Adaptation [142.31610972922067]
  This work addresses the unsupervised domain adaptation problem, especially in the case of class labels in the target domain being only a subset of those in the source domain. We build on domain adversarial learning and propose a novel domain adaptation method BA$3$US with two new techniques termed Balanced Adversarial Alignment (BAA) and Adaptive Uncertainty Suppression (AUS) Experimental results on multiple benchmarks demonstrate our BA$3$US surpasses state-of-the-arts for partial domain adaptation tasks.
  arXiv  Detail & Related papers  (2020-03-05T11:37:06Z)
• A Primer on Domain Adaptation [0.0]
  supervised machine learning assumes that the distribution of the source samples used to train an algorithm is the same as the one of the target samples on which it is supposed to make predictions. The myriad of methods available and the unfortunate lack of a clear and universally accepted terminology can however make the topic rather daunting for the newcomer.
  arXiv  Detail & Related papers  (2020-01-27T08:10:18Z)

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.