Effective Sample Size, Dimensionality, and Generalization in Covariate Shift Adaptation

• URL: http://arxiv.org/abs/2010.01184v5
• Date: Sun, 9 Jan 2022 02:38:53 GMT
• Title: Effective Sample Size, Dimensionality, and Generalization in Covariate Shift Adaptation
• Authors: Felipe Maia Polo, Renato Vicente
• Abstract summary: We show how effective sample size, dimensionality, and model performance/generalization are formally related in supervised learning. In this paper, we focus on building a unified view connecting the ESS, data dimensionality, and generalization.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In supervised learning, training and test datasets are often sampled from distinct distributions. Domain adaptation techniques are thus required. Covariate shift adaptation yields good generalization performance when domains differ only by the marginal distribution of features. Covariate shift adaptation is usually implemented using importance weighting, which may fail, according to common wisdom, due to small effective sample sizes (ESS). Previous research argues this scenario is more common in high-dimensional settings. However, how effective sample size, dimensionality, and model performance/generalization are formally related in supervised learning, considering the context of covariate shift adaptation, is still somewhat obscure in the literature. Thus, a main challenge is presenting a unified theory connecting those points. Hence, in this paper, we focus on building a unified view connecting the ESS, data dimensionality, and generalization in the context of covariate shift adaptation. Moreover, we also demonstrate how dimensionality reduction or feature selection can increase the ESS, and argue that our results support dimensionality reduction before covariate shift adaptation as a good practice.

Related papers

• Transformation-Invariant Learning and Theoretical Guarantees for OOD Generalization [34.036655200677664]
  This paper focuses on a distribution shift setting where train and test distributions can be related by classes of (data) transformation maps. We establish learning rules and algorithmic reductions to Empirical Risk Minimization (ERM) We highlight that the learning rules we derive offer a game-theoretic viewpoint on distribution shift.
  arXiv  Detail & Related papers  (2024-10-30T20:59:57Z)
• Information Geometrically Generalized Covariate Shift Adaptation [5.990174495635326]
  Many machine learning methods assume that the training and test data follow the same distribution. We show that the well-known family of covariate shift adaptation methods is unified in the framework of information geometry. Numerical experiments show that our generalization can achieve better performance than the existing methods it encompasses.
  arXiv  Detail & Related papers  (2023-04-19T02:52:54Z)
• Supervised Contrastive Learning with Heterogeneous Similarity for Distribution Shifts [3.7819322027528113]
  We propose a new regularization using the supervised contrastive learning to prevent such overfitting and to train models that do not degrade their performance under the distribution shifts. Experiments on benchmark datasets that emulate distribution shifts, including subpopulation shift and domain generalization, demonstrate the advantage of the proposed method.
  arXiv  Detail & Related papers  (2023-04-07T01:45:09Z)
• Modeling Uncertain Feature Representation for Domain Generalization [49.129544670700525]
  We show that our method consistently improves the network generalization ability on multiple vision tasks. Our methods are simple yet effective and can be readily integrated into networks without additional trainable parameters or loss constraints.
  arXiv  Detail & Related papers  (2023-01-16T14:25:02Z)
• Test-Time Adaptation with Shape Moments for Image Segmentation [16.794050614196916]
  We investigate test-time single-subject adaptation for segmentation. We propose a Shape-guided Entropy Minimization objective for tackling this task. We show the potential of integrating various shape priors to guide adaptation to plausible solutions.
  arXiv  Detail & Related papers  (2022-05-16T20:47:13Z)
• Towards Principled Disentanglement for Domain Generalization [90.9891372499545]
  A fundamental challenge for machine learning models is generalizing to out-of-distribution (OOD) data. We first formalize the OOD generalization problem as constrained optimization, called Disentanglement-constrained Domain Generalization (DDG) Based on the transformation, we propose a primal-dual algorithm for joint representation disentanglement and domain generalization.
  arXiv  Detail & Related papers  (2021-11-27T07:36:32Z)
• Variational Disentanglement for Domain Generalization [68.85458536180437]
  We propose to tackle the problem of domain generalization by delivering an effective framework named Variational Disentanglement Network (VDN) VDN is capable of disentangling the domain-specific features and task-specific features, where the task-specific features are expected to be better generalized to unseen but related test data.
  arXiv  Detail & Related papers  (2021-09-13T09:55:32Z)
• Squared $\ell_2$ Norm as Consistency Loss for Leveraging Augmented Data to Learn Robust and Invariant Representations [76.85274970052762]
  Regularizing distance between embeddings/representations of original samples and augmented counterparts is a popular technique for improving robustness of neural networks. In this paper, we explore these various regularization choices, seeking to provide a general understanding of how we should regularize the embeddings. We show that the generic approach we identified (squared $ell$ regularized augmentation) outperforms several recent methods, which are each specially designed for one task.
  arXiv  Detail & Related papers  (2020-11-25T22:40:09Z)
• 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)
• Uniform Priors for Data-Efficient Transfer [65.086680950871]
  We show that features that are most transferable have high uniformity in the embedding space. We evaluate the regularization on its ability to facilitate adaptation to unseen tasks and data.
  arXiv  Detail & Related papers  (2020-06-30T04:39:36Z)

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.