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Tom Stepleton

2 accepted papers

2019

Wasserstein Fair Classification

UAI 2019poster

We propose an approach to fair classification that enforces independence between the classifier outputs and sensitive information by minimizing Wasserstein-1 distances. The approach has desirable theoretical properties and is robust to specific choices of the threshold used to obtain class predictio…

2016

Safe and Efficient Off-Policy Reinforcement Learning

NeurIPS 2016poster

In this work, we take a fresh look at some old and new algorithms for off-policy, return-based reinforcement learning. Expressing these in a common form, we derive a novel algorithm, Retrace(lambda), with three desired properties: (1) it has low variance; (2) it safely uses samples collected from an…

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