5 citations · 12 across the 8 of their papers we have counts for
8 papers
On Mitigating Affinity Bias through Bandits with Evolving Biased Feedback
Matthew Faw, Constantine Caramanis, Jessica Hoffmann
Unconscious bias has been shown to influence how we assess our peers, with consequences for hiring, promotions and admissions. In this work, we focus on affinity bias, the componen…
Fairness for Image Generation with Uncertain Sensitive Attributes
Ajil Jalal, Sushrut Karmalkar, Jessica Hoffmann +2
This work tackles the issue of fairness in the context of generative procedures, such as image super-resolution, which entail different definitions from the standard classification…
Quarantines as a Targeted Immunization Strategy
Jessica Hoffmann, Matt Jordan, Constantine Caramanis
In the context of the recent COVID-19 outbreak, quarantine has been used to "flatten the curve" and slow the spread of the disease. In this paper, we show that this is not the only…
Adversarial Graph Embeddings for Fair Influence Maximization over Social Networks
Moein Khajehnejad, Ahmad Asgharian Rezaei, Mahmoudreza Babaei +3
Influence maximization is a widely studied topic in network science, where the aim is to reach the maximum possible number of nodes, while only targeting a small initial set of ind…
Learning Mixtures of Graphs from Epidemic Cascades
Jessica Hoffmann, Soumya Basu, Surbhi Goel +1
We consider the problem of learning the weighted edges of a balanced mixture of two undirected graphs from epidemic cascades. While mixture models are popular modeling tools, algor…
Learning Graphs from Noisy Epidemic Cascades
Jessica Hoffmann, Constantine Caramanis
We consider the problem of learning the weighted edges of a graph by observing the noisy times of infection for multiple epidemic cascades on this graph. Past work has considered t…