arXiv:2608. 14211v1 Announce Type: cross Abstract: Tabular Foundation Models (TFMs) have emerged as leading methods for tabular predictive tasks, leveraging in-context learning to predict on new data without task-specific training.
arXiv:2604. 16780v2 Announce Type: replace-cross Abstract: This paper presents FairNVT, a lightweight debiasing framework for pretrained transformer-based encoders that improves prediction fairness while preserving task performance.
arXiv:2606. 28097v1 Announce Type: new Abstract: Post-hoc controllability of fair machine learning models, the ability to control the trade-off between fairness and accuracy after training, is valuable for practical deployment.
arXiv:2606. 00656v1 Announce Type: cross Abstract: Ensuring fair and equitable treatment across diverse groups, particularly in multi-class classification tasks, poses a significant challenge due to the persistent biases inherent in machine learning models.
arXiv:2604. 16610v2 Announce Type: replace-cross Abstract: Machine learning models often inherit biases from historical data, raising critical concerns about fairness and accountability.
arXiv:2602. 16794v2 Announce Type: replace-cross Abstract: Conformal prediction (CP) offers distribution-free uncertainty quantification for machine learning models, yet its interplay with fairness in downstream decision-making remains underexplored.
arXiv:2404. 01356v3 Announce Type: replace-cross Abstract: Deep neural networks are vulnerable to adversarial perturbations that can simultaneously degrade prediction robustness and individual fairness across diverse application settings.
arXiv:2509. 16462v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) are increasingly used in high-stakes decision-making systems, where biased predictions can reinforce social and economic disparities.
arXiv:2606. 00826v1 Announce Type: new Abstract: Strategic machine learning investigates scenarios where agents manipulate their features to receive favorable decisions from predictive models.
arXiv:2604. 27011v2 Announce Type: replace-cross Abstract: AutoML, intended as the process of automating the application of machine learning to real-world problems, is a key step for AI popularisation.
arXiv:2508. 12042v3 Announce Type: replace Abstract: Federated learning (FL) allows collaborative training of machine learning models across multiple parties without sharing raw data.
arXiv:2607. 07471v1 Announce Type: cross Abstract: Machine learning models are increasingly deployed in high-stakes domains, raising concerns about both privacy and fairness.
arXiv:2607. 29441v1 Announce Type: new Abstract: Many real-world systems rely on predictive models to inform decisions, and fairness concerns arise in both the prediction and decision stages.