@InProceedings{lengerich2019learning,
  title={Learning Sample-Specific Models with Low-Rank Personalized Regression},
  author={Lengerich, Benjamin J. and Aragam, Bryon and Xing, Eric P.},
  journal={Advances in Neural Information Processing Systems (NeurIPS)},
  booktitle={Advances in Neural Information Processing Systems (NeurIPS)},
  year={2019},
  informal_venue = {NeurIPS},
  abstract = {
    Modern applications of machine learning (ML) deal with increasingly heterogeneous datasets comprised of data collected from overlapping latent subpopulations. As a result, traditional models trained over large datasets may fail to recognize highly predictive localized effects in favour of weakly predictive global patterns. This is a problem because localized effects are critical to developing individualized policies and treatment plans in applications ranging from precision medicine to advertising. To address this challenge, we propose to estimate sample-specific models that tailor inference and prediction at the individual level. In contrast to classical ML models that estimate a single, complex model (or only a few complex models), our approach produces a model personalized to each sample. These sample-specific models can be studied to understand subgroup dynamics that go beyond coarse-grained class labels. Crucially, our approach does not assume that relationships between samples (e.g. a similarity network) are known a priori. Instead, we use unmodeled covariates to learn a latent distance metric over the samples. We apply this approach to financial, biomedical, and electoral data as well as simulated data and show that sample-specific models provide fine-grained interpretations of complicated phenomena without sacrificing predictive accuracy compared to state-of-the-art models such as deep neural networks.
  },
  url_paper = {https://proceedings.neurips.cc/paper/2019/file/52d2752b150f9c35ccb6869cbf074e48-Paper.pdf},
  url_preprint = {https://arxiv.org/abs/1910.06939},
  keywords = {Interpretable, Contextualized}
}

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As a result, traditional models trained over large datasets may fail to recognize highly predictive localized effects in favour of weakly predictive global patterns. This is a problem because localized effects are critical to developing individualized policies and treatment plans in applications ranging from precision medicine to advertising. To address this challenge, we propose to estimate sample-specific models that tailor inference and prediction at the individual level. In contrast to classical ML models that estimate a single, complex model (or only a few complex models), our approach produces a model personalized to each sample. These sample-specific models can be studied to understand subgroup dynamics that go beyond coarse-grained class labels. Crucially, our approach does not assume that relationships between samples (e.g. a similarity network) are known a priori. Instead, we use unmodeled covariates to learn a latent distance metric over the samples. We apply this approach to financial, biomedical, and electoral data as well as simulated data and show that sample-specific models provide fine-grained interpretations of complicated phenomena without sacrificing predictive accuracy compared to state-of-the-art models such as deep neural networks. ","url_paper":"https://proceedings.neurips.cc/paper/2019/file/52d2752b150f9c35ccb6869cbf074e48-Paper.pdf","url_preprint":"https://arxiv.org/abs/1910.06939","keywords":"Interpretable, Contextualized","bibtex":"@InProceedings{lengerich2019learning,\n title={Learning Sample-Specific Models with Low-Rank Personalized Regression},\n author={Lengerich, Benjamin J. and Aragam, Bryon and Xing, Eric P.},\n journal={Advances in Neural Information Processing Systems (NeurIPS)},\n booktitle={Advances in Neural Information Processing Systems (NeurIPS)},\n year={2019},\n informal_venue = {NeurIPS},\n abstract = {\n Modern applications of machine learning (ML) deal with increasingly heterogeneous datasets comprised of data collected from overlapping latent subpopulations. As a result, traditional models trained over large datasets may fail to recognize highly predictive localized effects in favour of weakly predictive global patterns. This is a problem because localized effects are critical to developing individualized policies and treatment plans in applications ranging from precision medicine to advertising. To address this challenge, we propose to estimate sample-specific models that tailor inference and prediction at the individual level. In contrast to classical ML models that estimate a single, complex model (or only a few complex models), our approach produces a model personalized to each sample. These sample-specific models can be studied to understand subgroup dynamics that go beyond coarse-grained class labels. Crucially, our approach does not assume that relationships between samples (e.g. a similarity network) are known a priori. Instead, we use unmodeled covariates to learn a latent distance metric over the samples. 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