8 papers
Training Fair Tabular Foundation Models
Patrik Kenfack, Jesse C. Cresswell, Anthony L. Caterini +2
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.…
Model Stealing Through the Lens of Model Multiplicity
Eliott Baltz, Satoshi Hara, Ulrich Aïvodji
Model stealing attacks, where adversaries create high-fidelity surrogate models, are a significant threat to the intellectual property of machine learning services. Conventional wi…
When Interpretability Is Unequally Distributed: Fairness in Hybrid Interpretable Models
Ziba Jabbar Zare, Ulrich Aïvodji, Julien Ferry +1
Hybrid interpretable models combine a transparent component with a black-box model by assigning some examples to the former and deferring the rest to the latter. While this design…
Test-Time Collective Action: Proxy-Based Perturbations for Correcting Algorithmic Harms
Meghana Bhange, Ulrich Aïvodji, Elliot Creager
When machine learning systems under-perform for particular subgroups, affected users typically have no way to correct these disparities without relying on platform-level fixes. Exi…
Conscious Data Contribution via Community-Driven Chain-of-Thought Distillation
Lena Libon, Meghana Bhange, Rushabh Solanki +2
The current era of AI development places a heavy emphasis on training large models on increasingly scaled-up datasets. This paradigm has catalyzed entirely new product categories,…
Towards Fair In-Context Learning with Tabular Foundation Models
Patrik Kenfack, Samira Ebrahimi Kahou, Ulrich Aïvodji
Transformer-based tabular foundation models have recently demonstrated promising in-context learning (ICL) performance on structured data, emerging as competitive alternatives to g…