566 citations · 2.5k across the 42 of their papers we have counts for
6 papers · 1 filter
Two-sided fairness in rankings via Lorenz dominance
Virginie Do, Sam Corbett-Davies, Jamal Atif +1
We consider the problem of generating rankings that are fair towards both users and item producers in recommender systems. We address both usual recommendation (e.g., of music or m…
Hierarchical Skills for Efficient Exploration
Jonas Gehring, Gabriel Synnaeve, Andreas Krause +1
In reinforcement learning, pre-trained low-level skills have the potential to greatly facilitate exploration. However, prior knowledge of the downstream task is required to strike…
Online Selection of Diverse Committees
Virginie Do, Jamal Atif, Jérôme Lang +1
Citizens' assemblies need to represent subpopulations according to their proportions in the general population. These large committees are often constructed in an online fashion by…
A Self-Supervised Auxiliary Loss for Deep RL in Partially Observable Settings
Eltayeb Ahmed, Luisa Zintgraf, Christian A. Schroeder de Witt +1
In this work we explore an auxiliary loss useful for reinforcement learning in environments where strong performing agents are required to be able to navigate a spatial environment…
Online certification of preference-based fairness for personalized recommender systems
Virginie Do, Sam Corbett-Davies, Jamal Atif +1
Recommender systems are facing scrutiny because of their growing impact on the opportunities we have access to. Current audits for fairness are limited to coarse-grained parity ass…
Gradient Matching for Domain Generalization
Yuge Shi, Jeffrey Seely, Philip H. S. Torr +4
Machine learning systems typically assume that the distributions of training and test sets match closely. However, a critical requirement of such systems in the real world is their…