Data Debiasing with Datamodels (D3M): Improving Subgroup Robustness via Data Selection

• URL: http://arxiv.org/abs/2406.16846v1
• Date: Mon, 24 Jun 2024 17:51:01 GMT
• Title: Data Debiasing with Datamodels (D3M): Improving Subgroup Robustness via Data Selection
• Authors: Saachi Jain, Kimia Hamidieh, Kristian Georgiev, Andrew Ilyas, Marzyeh Ghassemi, Aleksander Madry,
• Abstract summary: We introduce Data Debiasing with Datamodels (D3M), a debiasing approach which isolates and removes specific training examples that drive the model's failures on minority groups.
• Score: 80.85902083005237
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Machine learning models can fail on subgroups that are underrepresented during training. While techniques such as dataset balancing can improve performance on underperforming groups, they require access to training group annotations and can end up removing large portions of the dataset. In this paper, we introduce Data Debiasing with Datamodels (D3M), a debiasing approach which isolates and removes specific training examples that drive the model's failures on minority groups. Our approach enables us to efficiently train debiased classifiers while removing only a small number of examples, and does not require training group annotations or additional hyperparameter tuning.

Related papers

• Efficient Bias Mitigation Without Privileged Information [14.21628601482357]
  Deep neural networks trained via empirical risk minimisation often exhibit significant performance disparities across groups. Existing bias mitigation methods that aim to address this issue often rely on group labels for training or validation. We propose Targeted Augmentations for Bias Mitigation (TAB), a framework that leverages the entire training history of a helper model to identify spurious samples. We show that TAB improves worst-group performance without any group information or model selection, outperforming existing methods while maintaining overall accuracy.
  arXiv  Detail & Related papers  (2024-09-26T09:56:13Z)
• Data Diet: Can Trimming PET/CT Datasets Enhance Lesion Segmentation? [68.51625420337575]
  We describe our approach to compete in the autoPET3 datacentric track. We find that in the autoPETIII dataset, a model that is trained on the entire dataset exhibits undesirable characteristics. We counteract this by removing the easiest samples from the training dataset as measured by the model loss before retraining from scratch.
  arXiv  Detail & Related papers  (2024-09-20T14:47:58Z)
• Towards Last-layer Retraining for Group Robustness with Fewer Annotations [11.650659637480112]
  Empirical risk minimization (ERM) of neural networks is prone to over-reliance on spurious correlations. Recent deep feature reweighting technique achieves state-of-the-art group robustness via simple last-layer retraining. We show that last-layer retraining can greatly improve worst-group accuracy even when the reweighting dataset has only a small proportion of worst-group data.
  arXiv  Detail & Related papers  (2023-09-15T16:52:29Z)
• Ranking & Reweighting Improves Group Distributional Robustness [14.021069321266516]
  We propose a ranking-based training method called Discounted Rank Upweighting (DRU) to learn models that exhibit strong OOD performance on the test data. Results on several synthetic and real-world datasets highlight the superior ability of our group-ranking-based (akin to soft-minimax) approach in selecting and learning models that are robust to group distributional shifts.
  arXiv  Detail & Related papers  (2023-05-09T20:37:16Z)
• Dataless Knowledge Fusion by Merging Weights of Language Models [51.8162883997512]
  Fine-tuning pre-trained language models has become the prevalent paradigm for building downstream NLP models. This creates a barrier to fusing knowledge across individual models to yield a better single model. We propose a dataless knowledge fusion method that merges models in their parameter space.
  arXiv  Detail & Related papers  (2022-12-19T20:46:43Z)
• Outlier-Robust Group Inference via Gradient Space Clustering [50.87474101594732]
  Existing methods can improve the worst-group performance, but they require group annotations, which are often expensive and sometimes infeasible to obtain. We address the problem of learning group annotations in the presence of outliers by clustering the data in the space of gradients of the model parameters. We show that data in the gradient space has a simpler structure while preserving information about minority groups and outliers, making it suitable for standard clustering methods like DBSCAN.
  arXiv  Detail & Related papers  (2022-10-13T06:04:43Z)
• Take One Gram of Neural Features, Get Enhanced Group Robustness [23.541213868620837]
  Predictive performance of machine learning models trained with empirical risk minimization can degrade considerably under distribution shifts. We propose to partition the training dataset into groups based on Gram matrices of features extracted by an identification'' model. Our approach not only improves group robustness over ERM but also outperforms all recent baselines.
  arXiv  Detail & Related papers  (2022-08-26T12:34:55Z)
• Towards Group Robustness in the presence of Partial Group Labels [61.33713547766866]
  spurious correlations between input samples and the target labels wrongly direct the neural network predictions. We propose an algorithm that optimize for the worst-off group assignments from a constraint set. We show improvements in the minority group's performance while preserving overall aggregate accuracy across groups.
  arXiv  Detail & Related papers  (2022-01-10T22:04:48Z)
• Examining and Combating Spurious Features under Distribution Shift [94.31956965507085]
  We define and analyze robust and spurious representations using the information-theoretic concept of minimal sufficient statistics. We prove that even when there is only bias of the input distribution, models can still pick up spurious features from their training data. Inspired by our analysis, we demonstrate that group DRO can fail when groups do not directly account for various spurious correlations.
  arXiv  Detail & Related papers  (2021-06-14T05:39:09Z)

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.