4.3k citations · 10.2k across the 68 of their papers we have counts for
7 papers · 2 filters
Practical Compositional Fairness: Understanding Fairness in Multi-Component Recommender Systems
Xuezhi Wang, Nithum Thain, Anu Sinha +4
How can we build recommender systems to take into account fairness? Real-world recommender systems are often composed of multiple models, built by multiple teams. However, most res…
Toward a better trade-off between performance and fairness with kernel-based distribution matching
Flavien Prost, Hai Qian, Qiuwen Chen +3
As recent literature has demonstrated how classifiers often carry unintended biases toward some subgroups, deploying machine learned models to users demands careful consideration o…
Transfer of Machine Learning Fairness across Domains
Candice Schumann, Xuezhi Wang, Alex Beutel +3
If our models are used in new or unexpected cases, do we know if they will make fair predictions? Previously, researchers developed ways to debias a model for a single problem doma…
Quantifying Long Range Dependence in Language and User Behavior to improve RNNs
Francois Belletti, Minmin Chen, Ed H. Chi
Characterizing temporal dependence patterns is a critical step in understanding the statistical properties of sequential data. Long Range Dependence (LRD) --- referring to long-ran…
Towards Neural Mixture Recommender for Long Range Dependent User Sequences
Jiaxi Tang, Francois Belletti, Sagar Jain +4
Understanding temporal dynamics has proved to be highly valuable for accurate recommendation. Sequential recommenders have been successful in modeling the dynamics of users and ite…
Putting Fairness Principles into Practice: Challenges, Metrics, and Improvements
Alex Beutel, Jilin Chen, Tulsee Doshi +6
As more researchers have become aware of and passionate about algorithmic fairness, there has been an explosion in papers laying out new metrics, suggesting algorithms to address i…