11 citations · 17 across the 4 of their papers we have counts for
9 papers
Analyzing the Effect of Sampling in GNNs on Individual Fairness
Rebecca Salganik, Fernando Diaz, Golnoosh Farnadi
Graph neural network (GNN) based methods have saturated the field of recommender systems. The gains of these systems have been significant, showing the advantages of interpreting d…
PrivFair: a Library for Privacy-Preserving Fairness Auditing
Sikha Pentyala, David Melanson, Martine De Cock +1
Machine learning (ML) has become prominent in applications that directly affect people's quality of life, including in healthcare, justice, and finance. ML models have been found t…
Counterexample-Guided Learning of Monotonic Neural Networks
Aishwarya Sivaraman, Golnoosh Farnadi, Todd Millstein +1
The widespread adoption of deep learning is often attributed to its automatic feature construction with minimal inductive bias. However, in many real-world tasks, the learned funct…
User Profiling Using Hinge-loss Markov Random Fields
Golnoosh Farnadi, Lise Getoor, Marie-Francine Moens +1
A variety of approaches have been proposed to automatically infer the profiles of users from their digital footprint in social media. Most of the proposed approaches focus on minin…
Compiling Stochastic Constraint Programs to And-Or Decision Diagrams
Behrouz Babaki, Golnoosh Farnadi, Gilles Pesant
Factored stochastic constraint programming (FSCP) is a formalism to represent multi-stage decision making problems under uncertainty. FSCP models support factorized probabilistic m…
Learning Fair Naive Bayes Classifiers by Discovering and Eliminating Discrimination Patterns
YooJung Choi, Golnoosh Farnadi, Behrouz Babaki +1
As machine learning is increasingly used to make real-world decisions, recent research efforts aim to define and ensure fairness in algorithmic decision making. Existing methods of…