46 citations · 54 across the 5 of their papers we have counts for
5 papers
SOPE: Spectrum of Off-Policy Estimators
Christina J. Yuan, Yash Chandak, Stephen Giguere +2
Many sequential decision making problems are high-stakes and require off-policy evaluation (OPE) of a new policy using historical data collected using some other policy. One of the…
Distributional Depth-Based Estimation of Object Articulation Models
Ajinkya Jain, Stephen Giguere, Rudolf Lioutikov +1
We propose a method that efficiently learns distributions over articulation model parameters directly from depth images without the need to know articulation model categories a pri…
Fairkit, Fairkit, on the Wall, Who's the Fairest of Them All? Supporting Data Scientists in Training Fair Models
Brittany Johnson, Jesse Bartola, Rico Angell +4
Modern software relies heavily on data and machine learning, and affects decisions that shape our world. Unfortunately, recent studies have shown that because of biases in data, so…
A Manifold Approach to Learning Mutually Orthogonal Subspaces
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…
Proximal Reinforcement Learning: A New Theory of Sequential Decision Making in Primal-Dual Spaces
Sridhar Mahadevan, Bo Liu, Philip Thomas +5
In this paper, we set forth a new vision of reinforcement learning developed by us over the past few years, one that yields mathematically rigorous solutions to longstanding import…