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Preferential Mixture-of-Experts: Interpretable Models that Rely on Human Expertise as much as Possible
We propose Preferential MoE, a novel human-ML mixture-of-experts model that augments human expertise in decision making with a data-based classifier only when necessary for predictive performance.
Prediction-Constrained Topic Models for Antidepressant Recommendation
Supervisory signals can help topic models discover low-dimensional data representations that are more interpretable for clinical tasks.
Prediction-Constrained Training for Semi-Supervised Mixture and Topic Models
Supervisory signals have the potential to make low-dimensional data representations, like those learned by mixture and topic models, more interpretable and useful.
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