4 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.…
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…
Adaptive Group Robust Ensemble Knowledge Distillation
Patrik Kenfack, Ulrich Aïvodji, Samira Ebrahimi Kahou
Neural networks can learn spurious correlations in the data, often leading to performance degradation for underrepresented subgroups. Studies have demonstrated that the disparity i…
Survey on AI Ethics: A Socio-technical Perspective
Dave Mbiazi, Meghana Bhange, Maryam Babaei +3
The past decade has observed a significant advancement in AI with deep learning-based models being deployed in diverse scenarios, including safety-critical applications. As these A…