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Oral
EDDI: Efficient Dynamic Discovery of High-Value Information with Partial VAE
Chao Ma · Sebastian Tschiatschek · Konstantina Palla · Jose Miguel Hernandez-Lobato · Sebastian Nowozin · Cheng Zhang

Wed Jun 12 11:40 AM -- 12:00 PM (PDT) @ Hall A
in Deep Generative Models »

Making decisions requires information relevant to the task at hand. Many real-life decision-making situations allow further relevant information to be acquired at a specific cost. For example, in assessing the health status of a patient we may decide to take additional measurements such as diagnostic tests or imaging scans before making a final assessment. Acquiring more relevant information enables better decision making, but may be costly. How can we trade off the desire to make good decisions by acquiring further information with the cost of performing that acquisition?

To this end, we propose a principled framework, named EDDI (Efficient Dynamic Discovery of high-value Information), based on the theory of Bayesian experimental design. In EDDI we propose a novel \emph{partial variational autoencoder} (Partial VAE), to efficiently handle missing data with different missing patterns. EDDI combines this Partial VAE with an acquisition function that maximizes expected information gain on a set of target variables. EDDI is efficient and demonstrates that dynamic discovery of high-value information is possible; we show cost reduction at the same decision quality and improved decision quality at the same cost in benchmarks and two health-care applications. We are confident that there is great potential for realizing these gains in real-world decision support systems.

Author Information

Chao Ma (University of Cambridge)
Sebastian Tschiatschek (Microsoft Research)
Konstantina Palla (Microsoft Research Cambridge)
Jose Miguel Hernandez-Lobato (University of Cambridge)
Sebastian Nowozin (MSR Cambridge)
Cheng Zhang (Microsoft Research, Cambridge)

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