32 citations · 33 across the 5 of their papers we have counts for
5 papers
Generating Diverse Negations from Affirmative Sentences
Darian Rodriguez Vasquez, Afroditi Papadaki
Despite the impressive performance of large language models across various tasks, they often struggle with reasoning under negated statements. Negations are important in real-world…
Federated Fairness without Access to Sensitive Groups
Afroditi Papadaki, Natalia Martinez, Martin Bertran +2
Current approaches to group fairness in federated learning assume the existence of predefined and labeled sensitive groups during training. However, due to factors ranging from eme…
Minimax Demographic Group Fairness in Federated Learning
Afroditi Papadaki, Natalia Martinez, Martin Bertran +2
Federated learning is an increasingly popular paradigm that enables a large number of entities to collaboratively learn better models. In this work, we study minimax group fairness…
Federating for Learning Group Fair Models
Afroditi Papadaki, Natalia Martinez, Martin Bertran +2
Federated learning is an increasingly popular paradigm that enables a large number of entities to collaboratively learn better models. In this work, we study minmax group fairness…
Learning to Collaborate for User-Controlled Privacy
Martin Bertran, Natalia Martinez, Afroditi Papadaki +3
It is becoming increasingly clear that users should own and control their data. Utility providers are also becoming more interested in guaranteeing data privacy. As such, users and…