104 citations · 129 across the 6 of their papers we have counts for
3 papers · 1 filter
POPCORN: Partially Observed Prediction COnstrained ReiNforcement Learning
Joseph Futoma, Michael C. Hughes, Finale Doshi-Velez
Many medical decision-making tasks can be framed as partially observed Markov decision processes (POMDPs). However, prevailing two-stage approaches that first learn a POMDP and the…
Beyond Sparsity: Tree Regularization of Deep Models for Interpretability
Mike Wu, Michael C. Hughes, Sonali Parbhoo +3
The lack of interpretability remains a key barrier to the adoption of deep models in many applications. In this work, we explicitly regularize deep models so human users might step…
Prediction-Constrained Training for Semi-Supervised Mixture and Topic Models
Michael C. Hughes, Leah Weiner, Gabriel Hope +4
Supervisory signals have the potential to make low-dimensional data representations, like those learned by mixture and topic models, more interpretable and useful. We propose a fra…