133 citations · 208 across the 7 of their papers we have counts for
5 papers · 1 filter
Size and Depth Separation in Approximating Benign Functions with Neural Networks
Gal Vardi, Daniel Reichman, Toniann Pitassi +1
When studying the expressive power of neural networks, a main challenge is to understand how the size and depth of the network affect its ability to approximate real functions. How…
Causal Modeling for Fairness in Dynamical Systems
Elliot Creager, David Madras, Toniann Pitassi +1
In many application areas---lending, education, and online recommenders, for example---fairness and equity concerns emerge when a machine learning system interacts with a dynamical…
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…
Fairness Through Causal Awareness: Learning Latent-Variable Models for Biased Data
David Madras, Elliot Creager, Toniann Pitassi +1
How do we learn from biased data? Historical datasets often reflect historical prejudices; sensitive or protected attributes may affect the observed treatments and outcomes. Classi…
Learning Adversarially Fair and Transferable Representations
David Madras, Elliot Creager, Toniann Pitassi +1
In this paper, we advocate for representation learning as the key to mitigating unfair prediction outcomes downstream. Motivated by a scenario where learned representations are use…