438 citations · 1k across the 8 of their papers we have counts for
9 papers · 1 filter
Optimizing Long-term Social Welfare in Recommender Systems: A Constrained Matching Approach
Martin Mladenov, Elliot Creager, Omer Ben-Porat +3
Most recommender systems (RS) research assumes that a user's utility can be maximized independently of the utility of the other agents (e.g., other users, content providers). In re…
MIM: Mutual Information Machine
Micha Livne, Kevin Swersky, David J. Fleet
We introduce the Mutual Information Machine (MIM), a probabilistic auto-encoder for learning joint distributions over observations and latent variables. MIM reflects three design p…
Flexibly Fair Representation Learning by Disentanglement
Elliot Creager, David Madras, Jörn-Henrik Jacobsen +4
We consider the problem of learning representations that achieve group and subgroup fairness with respect to multiple sensitive attributes. Taking inspiration from the disentangled…
Learning Sparse Networks Using Targeted Dropout
Aidan N. Gomez, Ivan Zhang, Siddhartha Rao Kamalakara +4
Neural networks are easier to optimise when they have many more weights than are required for modelling the mapping from inputs to outputs. This suggests a two-stage learning proce…
Neural Networks for Modeling Source Code Edits
Rui Zhao, David Bieber, Kevin Swersky +1
Programming languages are emerging as a challenging and interesting domain for machine learning. A core task, which has received significant attention in recent years, is building…
Meta-Dataset: A Dataset of Datasets for Learning to Learn from Few Examples
Eleni Triantafillou, Tyler Zhu, Vincent Dumoulin +8
Few-shot classification refers to learning a classifier for new classes given only a few examples. While a plethora of models have emerged to tackle it, we find the procedure and d…