46 citations · 54 across the 5 of their papers we have counts for
4 papers · 1 filter
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…
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…