When Are Learning Biases Equivalent? A Unifying Framework for Fairness, Robustness, and Distribution Shift
Fuente:
arXiv
Saved in:
| Main Author: | Mehta, Sushant |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Scaling Laws and In-Context Learning: A Unified Theoretical Framework
by: Mehta, Sushant, et al.
Published: (2025)
by: Mehta, Sushant, et al.
Published: (2025)
Beyond Surface-Level Similarity: Hierarchical Contamination Detection for Synthetic Training Data in Foundation Models
by: Mehta, Sushant
Published: (2025)
by: Mehta, Sushant
Published: (2025)
RuleFuser: An Evidential Bayes Approach for Rule Injection in Imitation Learned Planners and Predictors for Robustness under Distribution Shifts
by: Patrikar, Jay, et al.
Published: (2024)
by: Patrikar, Jay, et al.
Published: (2024)
LoRD: Adapting Differentiable Driving Policies to Distribution Shifts
by: Diehl, Christopher, et al.
Published: (2024)
by: Diehl, Christopher, et al.
Published: (2024)
Graph Fairness Learning under Distribution Shifts
by: Li, Yibo, et al.
Published: (2024)
by: Li, Yibo, et al.
Published: (2024)
Supervised Algorithmic Fairness in Distribution Shifts: A Survey
by: Shao, Minglai, et al.
Published: (2024)
by: Shao, Minglai, et al.
Published: (2024)
Towards Understanding Subliminal Learning: When and How Hidden Biases Transfer
by: Schrodi, Simon, et al.
Published: (2025)
by: Schrodi, Simon, et al.
Published: (2025)
UniAlign: A Model-Agnostic Framework for Robust Network Traffic Classification under Distribution Shifts
by: Wang, Tongze, et al.
Published: (2026)
by: Wang, Tongze, et al.
Published: (2026)
OMPO: A Unified Framework for RL under Policy and Dynamics Shifts
by: Luo, Yu, et al.
Published: (2024)
by: Luo, Yu, et al.
Published: (2024)
When Dynamics Shift, Robust Task Inference Wins: Offline Imitation Learning with Behavior Foundation Models Revisited
by: Agrawal, Rishabh, et al.
Published: (2026)
by: Agrawal, Rishabh, et al.
Published: (2026)
FairFlow: Mitigating Dataset Biases through Undecided Learning
by: Cheng, Jiali, et al.
Published: (2025)
by: Cheng, Jiali, et al.
Published: (2025)
On the Inductive Biases of Demographic Parity-based Fair Learning Algorithms
by: Lei, Haoyu, et al.
Published: (2024)
by: Lei, Haoyu, et al.
Published: (2024)
UFO: A Unified Flow-Oriented Framework for Robust Continual Graph Learning
by: Zhang, Danhui, et al.
Published: (2026)
by: Zhang, Danhui, et al.
Published: (2026)
Towards Collaborative Fairness in Federated Learning Under Imbalanced Covariate Shift
by: Yu, Tianrun, et al.
Published: (2025)
by: Yu, Tianrun, et al.
Published: (2025)
Robust Calibration For Improved Weather Prediction Under Distributional Shift
by: Gilda, Sankalp, et al.
Published: (2024)
by: Gilda, Sankalp, et al.
Published: (2024)
FairPO: Robust Preference Optimization for Fair Multi-Label Learning
by: Mondal, Soumen Kumar, et al.
Published: (2025)
by: Mondal, Soumen Kumar, et al.
Published: (2025)
Sufficient Invariant Learning for Distribution Shift
by: Kim, Taero, et al.
Published: (2022)
by: Kim, Taero, et al.
Published: (2022)
A Unifying Human-Centered AI Fairness Framework
by: Rahman, Munshi Mahbubur, et al.
Published: (2025)
by: Rahman, Munshi Mahbubur, et al.
Published: (2025)
Recovering from Biased Data: Can Fairness Constraints Improve Accuracy?
by: Blum, Avrim, et al.
Published: (2019)
by: Blum, Avrim, et al.
Published: (2019)
Assessing the Robustness of Climate Foundation Models under No-Analog Distribution Shifts
by: Navarro, Maria Conchita Agana, et al.
Published: (2026)
by: Navarro, Maria Conchita Agana, et al.
Published: (2026)
Robust Uncertainty Estimation under Distribution Shift via Difference Reconstruction
by: Xu, Xinran, et al.
Published: (2026)
by: Xu, Xinran, et al.
Published: (2026)
How Far Can Fairness Constraints Help Recover From Biased Data?
by: Sharma, Mohit, et al.
Published: (2023)
by: Sharma, Mohit, et al.
Published: (2023)
"What is Different Between These Datasets?" A Framework for Explaining Data Distribution Shifts
by: Babbar, Varun, et al.
Published: (2024)
by: Babbar, Varun, et al.
Published: (2024)
Distributions as Actions: A Unified Framework for Diverse Action Spaces
by: He, Jiamin, et al.
Published: (2025)
by: He, Jiamin, et al.
Published: (2025)
On ADMM in Heterogeneous Federated Learning: Personalization, Robustness, and Fairness
by: Zhu, Shengkun, et al.
Published: (2024)
by: Zhu, Shengkun, et al.
Published: (2024)
Learning Divergence Fields for Shift-Robust Graph Representations
by: Wu, Qitian, et al.
Published: (2024)
by: Wu, Qitian, et al.
Published: (2024)
Fairness under Covariate Shift: Improving Fairness-Accuracy tradeoff with few Unlabeled Test Samples
by: Havaldar, Shreyas, et al.
Published: (2023)
by: Havaldar, Shreyas, et al.
Published: (2023)
EnterpriseBench Corecraft: Training Generalizable Agents on High-Fidelity RL Environments
by: Mehta, Sushant, et al.
Published: (2026)
by: Mehta, Sushant, et al.
Published: (2026)
ABE: A Unified Framework for Robust and Faithful Attribution-Based Explainability
by: Zhu, Zhiyu, et al.
Published: (2025)
by: Zhu, Zhiyu, et al.
Published: (2025)
A Unifying Framework for Learning Argumentation Semantics
by: Mileva, Zlatina, et al.
Published: (2023)
by: Mileva, Zlatina, et al.
Published: (2023)
Addressing Label Shift in Distributed Learning via Entropy Regularization
by: Wu, Zhiyuan, et al.
Published: (2025)
by: Wu, Zhiyuan, et al.
Published: (2025)
Graph Data Augmentation with Contrastive Learning on Covariate Distribution Shift
by: Zeng, Fanlong, et al.
Published: (2025)
by: Zeng, Fanlong, et al.
Published: (2025)
Spectral Invariant Learning for Dynamic Graphs under Distribution Shifts
by: Zhang, Zeyang, et al.
Published: (2024)
by: Zhang, Zeyang, et al.
Published: (2024)
Beyond Accuracy: A Multi-Dimensional Framework for Evaluating Enterprise Agentic AI Systems
by: Mehta, Sushant
Published: (2025)
by: Mehta, Sushant
Published: (2025)
The Update-Equivalence Framework for Decision-Time Planning
by: Sokota, Samuel, et al.
Published: (2023)
by: Sokota, Samuel, et al.
Published: (2023)
Generative Distribution Prediction: A Unified Approach to Multimodal Learning
by: Tian, Xinyu, et al.
Published: (2025)
by: Tian, Xinyu, et al.
Published: (2025)
A Distributionally Robust Framework for Nuisance in Causal Effect Estimation
by: Tanimoto, Akira
Published: (2025)
by: Tanimoto, Akira
Published: (2025)
Calibrated Credit Intelligence: Shift-Robust and Fair Risk Scoring with Bayesian Uncertainty and Gradient Boosting
by: Nayak, Srikumar
Published: (2026)
by: Nayak, Srikumar
Published: (2026)
Learning Fair Invariant Representations under Covariate and Correlation Shifts Simultaneously
by: Li, Dong, et al.
Published: (2024)
by: Li, Dong, et al.
Published: (2024)
The Majority Vote Paradigm Shift: When Popular Meets Optimal
by: Purificato, Antonio, et al.
Published: (2025)
by: Purificato, Antonio, et al.
Published: (2025)
Similar Items
-
Scaling Laws and In-Context Learning: A Unified Theoretical Framework
by: Mehta, Sushant, et al.
Published: (2025) -
Beyond Surface-Level Similarity: Hierarchical Contamination Detection for Synthetic Training Data in Foundation Models
by: Mehta, Sushant
Published: (2025) -
RuleFuser: An Evidential Bayes Approach for Rule Injection in Imitation Learned Planners and Predictors for Robustness under Distribution Shifts
by: Patrikar, Jay, et al.
Published: (2024) -
LoRD: Adapting Differentiable Driving Policies to Distribution Shifts
by: Diehl, Christopher, et al.
Published: (2024) -
Graph Fairness Learning under Distribution Shifts
by: Li, Yibo, et al.
Published: (2024)