arXiv AI
Jun 8
Detecting and Mitigating Bias by Treating Fairness as a Symmetry Operation
arXiv:2606. 06514v1 Announce Type: new Abstract: Machine learning systems deployed in high stakes socioeconomic settings routinely display bias.
Fairness is Not Flat: Geometric Phase Transitions Against Shortcut Learning
arXiv:2604. 11704v2 Announce Type: replace-cross Abstract: Deep Neural Networks are highly susceptible to shortcut learning, frequently memorizing low-dimensional spurious correlations instead of underlying causal mechanisms.
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arXiv Machine Learning
Jul 17
Grad2Fair: A Gradient-driven Approach for Graph Fairness without Demographics
arXiv:2607. 14705v1 Announce Type: new Abstract: Graph neural networks (GNNs) frequently encounter group fairness issues, often yielding biased predictions against specific demographic groups defined by sensitive attributes such as gender or race.
arXiv Machine Learning
Aug 4
Mitigating Backdoors via Decoy Shortcuts and Knowledge Decoupling
arXiv:2608. 00732v1 Announce Type: new Abstract: Backdoor attacks pose a serious threat to deep neural networks, especially when training relies on third-party data, allowing adversaries to inject malicious behaviors through data poisoning.
Hugging Face Trending Papers
Jul 22
The Quadrilateral Loss: Additivity as a Measurable Behavior of Dense Neural Networks
Additive models buy interpretability by forbidding feature interactions, a constraint that neural instantiations enforce architecturally. We introduce the quadrilateral loss, a differentiable penalty that treats additivity as a measurable behavior instead: a second-order mixed difference on pairs of training points swapping one coordinate, which vanishes if and only if the coordinate carries no interaction, remains informative for piecewise-linear networks, and equals in expectation the per-coordinate interaction mass of the interventional Shapley-GAM.
arXiv AI
Jun 24
Tuning without Peeking: Provable Generalization Bounds and Robust LLM Post-Training
arXiv:2507. 01752v4 Announce Type: replace-cross Abstract: Gradient-based optimization is the workhorse of deep learning, offering efficient and scalable training via backpropagation.
arXiv Machine Learning
Jul 14
Fairness Constraints in High-Dimensional Generalized Linear 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 AI
Jun 2
Perturbation Effects on Accuracy and Fairness among Similar Individuals
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 AI
Jul 1
Perturbation Effects on Robustness and Individual Fairness
arXiv:2404. 01356v4 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 AI
Jul 23
The Quadrilateral Loss: Additivity as a Measurable Behavior of Dense Neural Networks
arXiv:2607. 20201v1 Announce Type: cross Abstract: Additive models buy interpretability by forbidding feature interactions, a constraint that neural instantiations enforce architecturally.
arXiv Machine Learning
Jun 19
Data Bias Mitigation under Coverage Constraints & The Price of Fairness
arXiv:2606. 20461v1 Announce Type: new Abstract: Machine learning models have been shown to exhibit discriminatory outcomes or degraded performance for individuals at the intersection of multiple sensitive attributes, such as race and gender.
arXiv AI
2d ago
Training Fair Tabular Foundation Models
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 Machine Learning
Jun 19
Convex training of Lipschitz-regularized shallow neural networks
arXiv:2606. 19652v1 Announce Type: new Abstract: In this work, we introduce a training procedure for shallow neural networks that promotes robustness against adversarial attacks.