Cover Image

From Invariant Representations to Invariant Data: Provable Robustness to Spurious Correlations via Noisy Counterfactual Matching

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Bai, Ruqi, Ji, Yao, Zhou, Zeyu, Inouye, David I.
Format:	Preprint
Published:	2025
Subjects:	Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Counterfactual Fairness by Combining Factual and Counterfactual Predictions
by: Zhou, Zeyu, et al.
Published: (2024)

Towards Characterizing Domain Counterfactuals For Invertible Latent Causal Models
by: Zhou, Zeyu, et al.
Published: (2023)

Benchmarking Algorithms for Federated Domain Generalization
by: Bai, Ruqi, et al.
Published: (2023)

Flow-based Generative Modeling of Potential Outcomes and Counterfactuals
by: Wu, Dongze, et al.
Published: (2025)

Robust Collaborative Inference with Vertically Split Data Over Dynamic Device Environments
by: Ganguli, Surojit, et al.
Published: (2023)

Invariant Correlation of Representation with Label: Enhancing Domain Generalization in Noisy Environments
by: Jin, Gaojie, et al.
Published: (2024)

Training on Plausible Counterfactuals Removes Spurious Correlations
by: Sadiku, Shpresim, et al.
Published: (2025)

Freeze then Train: Towards Provable Representation Learning under Spurious Correlations and Feature Noise
by: Ye, Haotian, et al.
Published: (2022)

Learning Counterfactually Invariant Predictors
by: Quinzan, Francesco, et al.
Published: (2022)

Elastic Representation: Mitigating Spurious Correlations for Group Robustness
by: Wen, Tao, et al.
Published: (2025)

Robust Invariant Representation Learning by Distribution Extrapolation
by: Yoshida, Kotaro, et al.
Published: (2025)

Domain Generalization In Robust Invariant Representation
by: Gupta, Gauri, et al.
Published: (2023)

Identifying Spurious Correlations using Counterfactual Alignment
by: Cohen, Joseph Paul, et al.
Published: (2023)

Superclass-Guided Representation Disentanglement for Spurious Correlation Mitigation
by: Liu, Chenruo, et al.
Published: (2025)

Improving Group Robustness on Spurious Correlation via Evidential Alignment
by: Ye, Wenqian, et al.
Published: (2025)

Mitigating Spurious Correlations in LLMs via Causality-Aware Post-Training
by: Gui, Shurui, et al.
Published: (2025)

Out of Spuriousity: Improving Robustness to Spurious Correlations without Group Annotations
by: Le, Phuong Quynh, et al.
Published: (2024)

Towards Practical Non-Adversarial Distribution Matching
by: Gong, Ziyu, et al.
Published: (2023)

Expressive Score-Based Priors for Distribution Matching with Geometry-Preserving Regularization
by: Gong, Ziyu, et al.
Published: (2025)

Mitigating Spurious Correlations in NLI via LLM-Synthesized Counterfactuals and Dynamic Balanced Sampling
by: Jaimes, Christopher Román
Published: (2025)

Investigating the Robustness of Subtask Distillation under Spurious Correlation
by: Chormai, Pattarawat, et al.
Published: (2026)

Provably Robust and Plausible Counterfactual Explanations for Neural Networks via Robust Optimisation
by: Jiang, Junqi, et al.
Published: (2023)

Mitigating Spurious Correlation via Distributionally Robust Learning with Hierarchical Ambiguity Sets
by: Jo, Sung Ho, et al.
Published: (2025)

Severing Spurious Correlations with Data Pruning
by: Mulchandani, Varun, et al.
Published: (2025)

ICON: Invariant Counterfactual Optimization with Neuro-Symbolic Priors for Text-Based Person Search
by: Wang, Xiangyu, et al.
Published: (2026)

Continual Learning of Domain-Invariant Representations
by: Janetzky, Pascal, et al.
Published: (2026)

Navigating Shortcuts, Spurious Correlations, and Confounders: From Origins via Detection to Mitigation
by: Steinmann, David, et al.
Published: (2024)

The Group Robustness is in the Details: Revisiting Finetuning under Spurious Correlations
by: LaBonte, Tyler, et al.
Published: (2024)

Learning Fair Invariant Representations under Covariate and Correlation Shifts Simultaneously
by: Li, Dong, et al.
Published: (2024)

Permutation-Invariant Representation Learning for Robust and Privacy-Preserving Feature Selection
by: Liu, Rui, et al.
Published: (2025)

Unsupervised Learning of Group Invariant and Equivariant Representations
by: Winter, Robin, et al.
Published: (2022)

Your Attention Matters: to Improve Model Robustness to Noise and Spurious Correlations
by: Tamayo-Rousseau, Camilo, et al.
Published: (2025)

Improving Group Robustness on Spurious Correlation Requires Preciser Group Inference
by: Han, Yujin, et al.
Published: (2024)

Learning Invariant Representations of Graph Neural Networks via Cluster Generalization
by: Xia, Donglin, et al.
Published: (2024)

Nuisances via Negativa: Adjusting for Spurious Correlations via Data Augmentation
by: Puli, Aahlad, et al.
Published: (2022)

Provably Robust Bayesian Counterfactual Explanations under Model Changes
by: Duell, Jamie, et al.
Published: (2026)

Mitigating Spurious Correlations via Disagreement Probability
by: Han, Hyeonggeun, et al.
Published: (2024)

NoisyCoconut: Counterfactual Consensus via Latent Space Reasoning
by: Jerge, Michael, et al.
Published: (2026)

Spurious Correlation-Aware Embedding Regularization for Worst-Group Robustness
by: Park, Subeen, et al.
Published: (2025)

Provably Robust DPO: Aligning Language Models with Noisy Feedback
by: Chowdhury, Sayak Ray, et al.
Published: (2024)