Icebreaker: Element-wise Efficient Information Acquisition with a Bayesian Deep Latent Gaussian Model

NeurIPS 2019 Wenbo GongSebastian TschiatschekSebastian NowozinRichard E. TurnerJosé Miguel Hernández-LobatoCheng Zhang

In this paper, we address the ice-start problem, i.e., the challenge of deploying machine learning models when only a little or no training data is initially available, and acquiring each feature element of data is associated with costs. This setting is representative of the real-world machine learning applications... (read more)

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