mlsanityData to 14 Oct 2026

Target Conditioned Representation Independence (TCRI); From Domain-Invariant to Domain-General Representations

arXiv 2212.11342 ↗First posted 21 Dec 2022cs.LG cs.AI stat.MLcited by 3

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Abstract (arXiv)

We propose a Target Conditioned Representation Independence (TCRI) objective for domain generalization. TCRI addresses the limitations of existing domain generalization methods due to incomplete constraints. Specifically, TCRI implements regularizers motivated by conditional independence constraints that are sufficient to strictly learn complete sets of invariant mechanisms, which we show are necessary and sufficient for domain generalization. Empirically, we show that TCRI is effective on both synthetic and real-world data. TCRI is competitive with baselines in average accuracy while outperforming them in worst-domain accuracy, indicating desired cross-domain stability.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 20 of its 32 references that are in the map. First posted on arXiv 21 Dec 2022.

Builds on

20 of 32 references.

References · 20

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8 more
  1. 2016Unsupervised Domain Adaptation with Residual Transfer Networkscited by 341
  2. 2017Joint Distribution Optimal Transportation for Domain Adaptationcited by 282
  3. 2020The Risks of Invariant Risk Minimizationcited by 262
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  8. 2021An Online Learning Approach to Interpolation and Extrapolation in Domain Generalizationcited by 30

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Cited by year

Citing papers per year: 2023: 1; 2024: 1; 2025: 1.

3 papers.

  1. 2025A Language Anchor-Guided Method for Robust Noisy Domain Generalizationcited by 5
  2. 2023Context is Environmentcited by 1
  3. 2024Towards a Better Evaluation of Out-of-Domain Generalizationcited by 0