2 citations · 2 across the 2 of their papers we have counts for
6 papers · 1 filter
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…
Fairness Under Demographic Scarce Regime
Patrik Joslin Kenfack, Samira Ebrahimi Kahou, Ulrich Aïvodji
Most existing works on fairness assume the model has full access to demographic information. However, there exist scenarios where demographic information is partially available bec…
Adversarial Stacked Auto-Encoders for Fair Representation Learning
Patrik Joslin Kenfack, Adil Mehmood Khan, Rasheed Hussain +1
Training machine learning models with the only accuracy as a final goal may promote prejudices and discriminatory behaviors embedded in the data. One solution is to learn latent re…
On the Fairness of Generative Adversarial Networks (GANs)
Patrik Joslin Kenfack, Daniil Dmitrievich Arapov, Rasheed Hussain +2
Generative adversarial networks (GANs) are one of the greatest advances in AI in recent years. With their ability to directly learn the probability distribution of data, and then s…