1 code implementation • NeurIPS 2021 • Christina J. Yuan, Yash Chandak, Stephen Giguere, Philip S. Thomas, Scott Niekum
In this paper, we present a new perspective on this bias-variance trade-off and show the existence of a spectrum of estimators whose endpoints are SIS and IS.
no code implementations • ICLR 2022 • Stephen Giguere, Blossom Metevier, Yuriy Brun, Philip S. Thomas, Scott Niekum, Bruno Castro da Silva
Recent studies have demonstrated that using machine learning for social applications can lead to injustice in the form of racist, sexist, and otherwise unfair and discriminatory outcomes.
1 code implementation • 12 Aug 2021 • Ajinkya Jain, Stephen Giguere, Rudolf Lioutikov, Scott Niekum
Our core contributions include a novel representation for distributions over rigid body transformations and articulation model parameters based on screw theory, von Mises-Fisher distributions, and Stiefel manifolds.
1 code implementation • NeurIPS 2019 • Blossom Metevier, Stephen Giguere, Sarah Brockman, Ari Kobren, Yuriy Brun, Emma Brunskill, Philip S. Thomas
We present RobinHood, an ofﬂine contextual bandit algorithm designed to satisfy a broad family of fairness constraints.
no code implementations • 8 Mar 2017 • Stephen Giguere, Francisco Garcia, Sridhar Mahadevan
Although many machine learning algorithms involve learning subspaces with particular characteristics, optimizing a parameter matrix that is constrained to represent a subspace can be challenging.
no code implementations • NeurIPS 2013 • Philip S. Thomas, William C. Dabney, Stephen Giguere, Sridhar Mahadevan
Natural actor-critics are a popular class of policy search algorithms for finding locally optimal policies for Markov decision processes.