5 citations · 17 across the 8 of their papers we have counts for
9 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…
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…
RepFair-GAN: Mitigating Representation Bias in GANs Using Gradient Clipping
Patrik Joslin Kenfack, Kamil Sabbagh, Adín Ramírez Rivera +1
Fairness has become an essential problem in many domains of Machine Learning (ML), such as classification, natural language processing, and Generative Adversarial Networks (GANs).…
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…
DP-SGD vs PATE: Which Has Less Disparate Impact on Model Accuracy?
Archit Uniyal, Rakshit Naidu, Sasikanth Kotti +4
Recent advances in differentially private deep learning have demonstrated that application of differential privacy, specifically the DP-SGD algorithm, has a disparate impact on dif…
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…