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ICML 2018oral90 citations

Accurate Inference for Adaptive Linear Models

Yash Deshpande, Lester Mackey, Vasilis Syrgkanis, Matt Taddy

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Abstract

Estimators computed from adaptively collected data do not behave like their non-adaptive brethren.Rather, the sequential dependence of the collection policy can lead to severe distributional biases that persist even in the infinite data limit. We develop a general method –

BibTeX
@InProceedings{pmlr-v80-deshpande18a,
  title = 	 {Accurate Inference for Adaptive Linear Models},
  author =       {Deshpande, Yash and Mackey, Lester and Syrgkanis, Vasilis and Taddy, Matt},
  booktitle = 	 {Proceedings of the 35th International Conference on Machine Learning},
  pages = 	 {1194--1203},
  year = 	 {2018},
  editor = 	 {Dy, Jennifer and Krause, Andreas},
  volume = 	 {80},
  series = 	 {Proceedings of Machine Learning Research},
  month = 	 {10--15 Jul},
  publisher =    {PMLR},
  pdf = 	 {http://proceedings.mlr.press/v80/deshpande18a/deshpande18a.pdf},
  url = 	 {https://proceedings.mlr.press/v80/deshpande18a.html},
  abstract = 	 {Estimators computed from adaptively collected data do not behave like their non-adaptive brethren.Rather, the sequential dependence of the collection policy can lead to severe distributional biases that persist even in the infinite data limit. We develop a general method –
Accurate Inference for Adaptive Linear Models · ICML 2018