Should Bias Always be Eliminated? A Principled Framework to Use Data Bias for OOD Generation

• URL: http://arxiv.org/abs/2507.17001v1
• Date: Tue, 22 Jul 2025 20:17:48 GMT
• Title: Should Bias Always be Eliminated? A Principled Framework to Use Data Bias for OOD Generation
• Authors: Yan Li, Guangyi Chen, Yunlong Deng, Zijian Li, Zeyu Tang, Anpeng Wu, Kun Zhang,
• Abstract summary: We introduce a novel framework that strategically leverages bias to complement invariant representations during inference.<n>We validate our approach through experiments on both synthetic datasets and standard domain generalization benchmarks.
• Score: 14.271988618123512
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Most existing methods for adapting models to out-of-distribution (OOD) domains rely on invariant representation learning to eliminate the influence of biased features. However, should bias always be eliminated -- and if not, when should it be retained, and how can it be leveraged? To address these questions, we first present a theoretical analysis that explores the conditions under which biased features can be identified and effectively utilized. Building on this theoretical foundation, we introduce a novel framework that strategically leverages bias to complement invariant representations during inference. The framework comprises two key components that leverage bias in both direct and indirect ways: (1) using invariance as guidance to extract predictive ingredients from bias, and (2) exploiting identified bias to estimate the environmental condition and then use it to explore appropriate bias-aware predictors to alleviate environment gaps. We validate our approach through experiments on both synthetic datasets and standard domain generalization benchmarks. Results consistently demonstrate that our method outperforms existing approaches, underscoring its robustness and adaptability.

Related papers

• Recover Experimental Data with Selection Bias using Counterfactual Logic [12.014061086940817]
  We analyze how selection mechanisms in the observational world propagate to the counterfactual domain.<n>We propose principled methods for leveraging partially unbiased observational data to recover $P(Y*_x*)$ from biased experimental datasets.
  arXiv  Detail & Related papers  (2025-05-31T01:23:39Z)
• Identifying and Mitigating Social Bias Knowledge in Language Models [52.52955281662332]
  We propose a novel debiasing approach, Fairness Stamp (FAST), which enables fine-grained calibration of individual social biases.<n>FAST surpasses state-of-the-art baselines with superior debiasing performance.<n>This highlights the potential of fine-grained debiasing strategies to achieve fairness in large language models.
  arXiv  Detail & Related papers  (2024-08-07T17:14:58Z)
• MANO: Exploiting Matrix Norm for Unsupervised Accuracy Estimation Under Distribution Shifts [25.643876327918544]
  Leveraging the models' outputs, specifically the logits, is a common approach to estimating the test accuracy of a pre-trained neural network on out-of-distribution samples. Despite their ease of implementation and computational efficiency, current logit-based methods are vulnerable to overconfidence issues, leading to prediction bias. We propose MaNo which applies a data-dependent normalization on the logits to reduce prediction bias and takes the $L_p$ norm of the matrix of normalized logits as the estimation score.
  arXiv  Detail & Related papers  (2024-05-29T10:45:06Z)
• Fine-Grained Dynamic Framework for Bias-Variance Joint Optimization on Data Missing Not at Random [2.8165314121189247]
  In most practical applications such as recommendation systems, display advertising, and so forth, the collected data often contains missing values. We develop a systematic fine-grained dynamic learning framework to jointly optimize bias and variance.
  arXiv  Detail & Related papers  (2024-05-24T10:07:09Z)
• Towards Real-world Debiasing: Rethinking Evaluation, Challenge, and Solution [17.080528126651977]
  We introduce two novel real-world-inspired biases to bridge this gap and build a systematic evaluation framework for real-world debiasing.<n>We propose a simple yet effective approach named Debias in Destruction (DiD) to address the challenge.
  arXiv  Detail & Related papers  (2024-05-24T06:06:41Z)
• Improving Bias Mitigation through Bias Experts in Natural Language Understanding [10.363406065066538]
  We propose a new debiasing framework that introduces binary classifiers between the auxiliary model and the main model. Our proposed strategy improves the bias identification ability of the auxiliary model.
  arXiv  Detail & Related papers  (2023-12-06T16:15:00Z)
• Causality and Independence Enhancement for Biased Node Classification [56.38828085943763]
  We propose a novel Causality and Independence Enhancement (CIE) framework, applicable to various graph neural networks (GNNs) Our approach estimates causal and spurious features at the node representation level and mitigates the influence of spurious correlations. Our approach CIE not only significantly enhances the performance of GNNs but outperforms state-of-the-art debiased node classification methods.
  arXiv  Detail & Related papers  (2023-10-14T13:56:24Z)
• Delving into Identify-Emphasize Paradigm for Combating Unknown Bias [52.76758938921129]
  We propose an effective bias-conflicting scoring method (ECS) to boost the identification accuracy. We also propose gradient alignment (GA) to balance the contributions of the mined bias-aligned and bias-conflicting samples. Experiments are conducted on multiple datasets in various settings, demonstrating that the proposed solution can mitigate the impact of unknown biases.
  arXiv  Detail & Related papers  (2023-02-22T14:50:24Z)
• Ensembling over Classifiers: a Bias-Variance Perspective [13.006468721874372]
  We build upon the extension to the bias-variance decomposition by Pfau (2013) in order to gain crucial insights into the behavior of ensembles of classifiers. We show that conditional estimates necessarily incur an irreducible error. Empirically, standard ensembling reducesthe bias, leading us to hypothesize that ensembles of classifiers may perform well in part because of this unexpected reduction.
  arXiv  Detail & Related papers  (2022-06-21T17:46:35Z)
• Information-Theoretic Bias Reduction via Causal View of Spurious Correlation [71.9123886505321]
  We propose an information-theoretic bias measurement technique through a causal interpretation of spurious correlation. We present a novel debiasing framework against the algorithmic bias, which incorporates a bias regularization loss. The proposed bias measurement and debiasing approaches are validated in diverse realistic scenarios.
  arXiv  Detail & Related papers  (2022-01-10T01:19:31Z)
• General Greedy De-bias Learning [163.65789778416172]
  We propose a General Greedy De-bias learning framework (GGD), which greedily trains the biased models and the base model like gradient descent in functional space. GGD can learn a more robust base model under the settings of both task-specific biased models with prior knowledge and self-ensemble biased model without prior knowledge.
  arXiv  Detail & Related papers  (2021-12-20T14:47:32Z)
• Learning Overlapping Representations for the Estimation of Individualized Treatment Effects [97.42686600929211]
  Estimating the likely outcome of alternatives from observational data is a challenging problem. We show that algorithms that learn domain-invariant representations of inputs are often inappropriate. We develop a deep kernel regression algorithm and posterior regularization framework that substantially outperforms the state-of-the-art on a variety of benchmarks data sets.
  arXiv  Detail & Related papers  (2020-01-14T12:56:29Z)

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.