Efficient Modeling of Latent Information in Supervised Learning using Gaussian Processes

@inproceedings{Dai2017EfficientMO,
  title={Efficient Modeling of Latent Information in Supervised Learning using Gaussian Processes},
  author={Zhenwen Dai and Mauricio A. {\'A}lvarez and Neil D. Lawrence},
  booktitle={NIPS},
  year={2017}
}
Often in machine learning, data are collected as a combination of multiple conditions, e.g., the voice recordings of multiple persons, each labeled with an ID. How could we build a model that captures the latent information related to these conditions and generalize to a new one with few data? We present a new model called Latent Variable Multiple Output Gaussian Processes (LVMOGP) and that allows to jointly model multiple conditions for regression and generalize to a new condition with a few… CONTINUE READING
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