Causally-Aware Information Bottleneck for Domain Adaptation

• URL: http://arxiv.org/abs/2601.04361v1
• Date: Wed, 07 Jan 2026 19:54:58 GMT
• Title: Causally-Aware Information Bottleneck for Domain Adaptation
• Authors: Mohammad Ali Javidian,
• Abstract summary: We tackle a common domain adaptation setting in causal systems.<n>We aim to impute the target variable in the target domain from the remaining observed variables under various shifts.<n>Across synthetic and real datasets, our approach consistently attains accurate imputations.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We tackle a common domain adaptation setting in causal systems. In this setting, the target variable is observed in the source domain but is entirely missing in the target domain. We aim to impute the target variable in the target domain from the remaining observed variables under various shifts. We frame this as learning a compact, mechanism-stable representation. This representation preserves information relevant for predicting the target while discarding spurious variation. For linear Gaussian causal models, we derive a closed-form Gaussian Information Bottleneck (GIB) solution. This solution reduces to a canonical correlation analysis (CCA)-style projection and offers Directed Acyclic Graph (DAG)-aware options when desired. For nonlinear or non-Gaussian data, we introduce a Variational Information Bottleneck (VIB) encoder-predictor. This approach scales to high dimensions and can be trained on source data and deployed zero-shot to the target domain. Across synthetic and real datasets, our approach consistently attains accurate imputations, supporting practical use in high-dimensional causal models and furnishing a unified, lightweight toolkit for causal domain adaptation.

Related papers

• Progressive Conservative Adaptation for Evolving Target Domains [76.9274842289221]
  Conventional domain adaptation typically transfers knowledge from a source domain to a stationary target domain. Restoring and adapting to such target data results in escalating computational and resource consumption over time. We propose a simple yet effective approach, termed progressive conservative adaptation (PCAda)
  arXiv  Detail & Related papers  (2024-02-07T04:11:25Z)
• Source-Free and Image-Only Unsupervised Domain Adaptation for Category Level Object Pose Estimation [18.011044932979143]
  3DUDA is a method capable of adapting to a nuisance-ridden target domain without 3D or depth data. We represent object categories as simple cuboid meshes, and harness a generative model of neural feature activations. We show that our method simulates fine-tuning on a global pseudo-labeled dataset under mild assumptions.
  arXiv  Detail & Related papers  (2024-01-19T17:48:05Z)
• Towards Source-free Domain Adaptive Semantic Segmentation via Importance-aware and Prototype-contrast Learning [26.544837987747766]
  We propose an end-to-end source-free domain adaptation semantic segmentation method via Importance-Aware and Prototype-Contrast learning. The proposed IAPC framework effectively extracts domain-invariant knowledge from the well-trained source model and learns domain-specific knowledge from the unlabeled target domain.
  arXiv  Detail & Related papers  (2023-06-02T15:09:19Z)
• Source-Free Domain Adaptation via Distribution Estimation [106.48277721860036]
  Domain Adaptation aims to transfer the knowledge learned from a labeled source domain to an unlabeled target domain whose data distributions are different. Recently, Source-Free Domain Adaptation (SFDA) has drawn much attention, which tries to tackle domain adaptation problem without using source data. In this work, we propose a novel framework called SFDA-DE to address SFDA task via source Distribution Estimation.
  arXiv  Detail & Related papers  (2022-04-24T12:22:19Z)
• Instance Relation Graph Guided Source-Free Domain Adaptive Object Detection [79.89082006155135]
  Unsupervised Domain Adaptation (UDA) is an effective approach to tackle the issue of domain shift. UDA methods try to align the source and target representations to improve the generalization on the target domain. The Source-Free Adaptation Domain (SFDA) setting aims to alleviate these concerns by adapting a source-trained model for the target domain without requiring access to the source data.
  arXiv  Detail & Related papers  (2022-03-29T17:50:43Z)
• Source-Free Adaptation to Measurement Shift via Bottom-Up Feature Restoration [6.9871848733878155]
  Source-free domain adaptation (SFDA) aims to adapt a model trained on labelled data in a source domain to unlabelled data in a target domain without access to the source-domain data during adaptation. We propose Feature Restoration (FR) as it seeks to extract features with the same semantics from the target domain as were previously extracted from the source. We additionally propose Bottom-Up Feature Restoration (BUFR), a bottom-up training scheme for FR which boosts performance by preserving learnt structure in the later layers of a network.
  arXiv  Detail & Related papers  (2021-07-12T14:21:14Z)
• Transformer-Based Source-Free Domain Adaptation [134.67078085569017]
  We study the task of source-free domain adaptation (SFDA), where the source data are not available during target adaptation. We propose a generic and effective framework based on Transformer, named TransDA, for learning a generalized model for SFDA.
  arXiv  Detail & Related papers  (2021-05-28T23:06:26Z)
• Learning Domain-invariant Graph for Adaptive Semi-supervised Domain Adaptation with Few Labeled Source Samples [65.55521019202557]
  Domain adaptation aims to generalize a model from a source domain to tackle tasks in a related but different target domain. Traditional domain adaptation algorithms assume that enough labeled data, which are treated as the prior knowledge are available in the source domain. We propose a Domain-invariant Graph Learning (DGL) approach for domain adaptation with only a few labeled source samples.
  arXiv  Detail & Related papers  (2020-08-21T08:13:25Z)
• Do We Really Need to Access the Source Data? Source Hypothesis Transfer for Unsupervised Domain Adaptation [102.67010690592011]
  Unsupervised adaptationUDA (UDA) aims to leverage the knowledge learned from a labeled source dataset to solve similar tasks in a new unlabeled domain. Prior UDA methods typically require to access the source data when learning to adapt the model. This work tackles a practical setting where only a trained source model is available and how we can effectively utilize such a model without source data to solve UDA problems.
  arXiv  Detail & Related papers  (2020-02-20T03:13:58Z)

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.