Cumulative Meta-Learning from Active Learning Queries for Robustness to Spurious Correlations

• URL: http://arxiv.org/abs/2605.20771v1
• Date: Wed, 20 May 2026 06:14:12 GMT
• Title: Cumulative Meta-Learning from Active Learning Queries for Robustness to Spurious Correlations
• Abstract summary: Cumulative Active Meta-Learning (CAML) is an active-learning framework that uses queried examples to meta-learn the prior, or inductive bias, governing how the model adapts.<n>We show that CAML improves minority-group accuracy across spurious-correlation benchmarks and acquisition strategies.
• Score: 2.3234871809423585
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Spurious correlations in real-world datasets cause machine learning models to rely on irrelevant patterns, undermining reliability, generalization, and fairness. Active learning offers a promising way to address this failure mode by querying informative samples that distinguish core features from spurious ones. However, standard active-learning methods simply append queried examples to the labeled set, effectively updating only the likelihood term. In deep learning regimes, the influence of these informative samples can be diluted by the larger labeled set and memorized by overparameterized models. We propose Cumulative Active Meta-Learning (CAML), an active-learning framework that uses queried examples to meta-learn the prior, or inductive bias, governing how the model adapts. CAML casts each active-learning round as a meta-learning task: the current labeled set serves as meta-train data for adaptation, while the newly queried batch serves as meta-test data for evaluating generalization. Unlike conventional meta-learning, which treats tasks as independent and identically distributed, CAML exploits the sequential dependence between active-learning rounds by maintaining a cumulative inductive bias that is progressively refined. Theoretically, we show that this cumulative formulation introduces interaction terms that couple earlier meta-learned inductive biases with later query-induced objectives, capturing dependencies absent from standard meta-learning. Empirically, CAML improves minority-group accuracy across spurious-correlation benchmarks and acquisition strategies, with gains of up to 27.8% on Dominoes, 29.9% on Waterbirds, 14.3% on SpuCo, and 24.0% on CivilComments.

Related papers

• Hybrid Meta-learners for Estimating Heterogeneous Treatment Effects [1.9506923346234724]
  Estimating conditional average treatment effects (CATE) from observational data involves modeling decisions that differ from supervised learning.<n>Previous approaches can be grouped into two primary "meta-learner" paradigms that impose distinct inductive biases.<n>We introduce the Hybrid Learner (H-learner), a novel regularization strategy that interpolates between the direct and indirect regularizations depending on the dataset.
  arXiv  Detail & Related papers  (2025-06-16T16:37:20Z)
• What Do Learning Dynamics Reveal About Generalization in LLM Reasoning? [83.83230167222852]
  We find that a model's generalization behavior can be effectively characterized by a training metric we call pre-memorization train accuracy. By connecting a model's learning behavior to its generalization, pre-memorization train accuracy can guide targeted improvements to training strategies.
  arXiv  Detail & Related papers  (2024-11-12T09:52:40Z)
• Rethinking Meta-Learning from a Learning Lens [35.98940987691948]
  We consider how to bridge the gap between theoretical understanding and practical implementation of meta-learning.<n>We propose TRLearner, a plug-and-play method that leverages task relation to calibrate meta-learning.
  arXiv  Detail & Related papers  (2024-09-13T02:00:16Z)
• Spuriousness-Aware Meta-Learning for Learning Robust Classifiers [26.544938760265136]
  Spurious correlations are brittle associations between certain attributes of inputs and target variables. Deep image classifiers often leverage them for predictions, leading to poor generalization on the data where the correlations do not hold. Mitigating the impact of spurious correlations is crucial towards robust model generalization, but it often requires annotations of the spurious correlations in data.
  arXiv  Detail & Related papers  (2024-06-15T21:41:25Z)
• Conformal Meta-learners for Predictive Inference of Individual Treatment Effects [0.0]
  We investigate the problem of machine learning-based (ML) predictive inference on individual treatment effects (ITEs) We develop conformal meta-learners, a general framework for issuing predictive intervals for ITEs by applying the standard conformal prediction (CP) procedure on top of CATE meta-learners.
  arXiv  Detail & Related papers  (2023-08-28T20:32:22Z)
• Scalable PAC-Bayesian Meta-Learning via the PAC-Optimal Hyper-Posterior: From Theory to Practice [54.03076395748459]
  A central question in the meta-learning literature is how to regularize to ensure generalization to unseen tasks. We present a generalization bound for meta-learning, which was first derived by Rothfuss et al. We provide a theoretical analysis and empirical case study under which conditions and to what extent these guarantees for meta-learning improve upon PAC-Bayesian per-task learning bounds.
  arXiv  Detail & Related papers  (2022-11-14T08:51:04Z)
• Meta-Learning with Self-Improving Momentum Target [72.98879709228981]
  We propose Self-improving Momentum Target (SiMT) to improve the performance of a meta-learner. SiMT generates the target model by adapting from the temporal ensemble of the meta-learner. We show that SiMT brings a significant performance gain when combined with a wide range of meta-learning methods.
  arXiv  Detail & Related papers  (2022-10-11T06:45:15Z)
• CMW-Net: Learning a Class-Aware Sample Weighting Mapping for Robust Deep Learning [55.733193075728096]
  Modern deep neural networks can easily overfit to biased training data containing corrupted labels or class imbalance. Sample re-weighting methods are popularly used to alleviate this data bias issue. We propose a meta-model capable of adaptively learning an explicit weighting scheme directly from data.
  arXiv  Detail & Related papers  (2022-02-11T13:49:51Z)
• BAMLD: Bayesian Active Meta-Learning by Disagreement [39.59987601426039]
  This paper introduces an information-theoretic active task selection mechanism to decrease the number of labeling requests for meta-training tasks. We report its empirical performance results that compare favourably against existing acquisition mechanisms.
  arXiv  Detail & Related papers  (2021-10-19T13:06:51Z)
• Improving Generalization in Meta-learning via Task Augmentation [69.83677015207527]
  We propose two task augmentation methods, including MetaMix and Channel Shuffle. Both MetaMix and Channel Shuffle outperform state-of-the-art results by a large margin across many datasets.
  arXiv  Detail & Related papers  (2020-07-26T01:50:42Z)
• Meta-Learned Confidence for Few-shot Learning [60.6086305523402]
  A popular transductive inference technique for few-shot metric-based approaches, is to update the prototype of each class with the mean of the most confident query examples. We propose to meta-learn the confidence for each query sample, to assign optimal weights to unlabeled queries. We validate our few-shot learning model with meta-learned confidence on four benchmark datasets.
  arXiv  Detail & Related papers  (2020-02-27T10:22:17Z)
• Incremental Meta-Learning via Indirect Discriminant Alignment [118.61152684795178]
  We develop a notion of incremental learning during the meta-training phase of meta-learning. Our approach performs favorably at test time as compared to training a model with the full meta-training set.
  arXiv  Detail & Related papers  (2020-02-11T01:39:12Z)

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.