Streaming Inference for Infinite Feature Models

Rylan Schaeffer; Yilun Du; Gabrielle K Liu; Ila Fiete

ICML 2022spotlight3 citations

Streaming Inference for Infinite Feature Models

Rylan Schaeffer, Yilun Du, Gabrielle K Liu, Ila Fiete

Abstract

Unsupervised learning from a continuous stream of data is arguably one of the most common and most challenging problems facing intelligent agents. One class of unsupervised models, collectively termed

BibTeX

@InProceedings{pmlr-v162-schaeffer22a,
  title = 	 {Streaming Inference for Infinite Feature Models},
  author =       {Schaeffer, Rylan and Du, Yilun and Liu, Gabrielle K and Fiete, Ila},
  booktitle = 	 {Proceedings of the 39th International Conference on Machine Learning},
  pages = 	 {19366--19387},
  year = 	 {2022},
  editor = 	 {Chaudhuri, Kamalika and Jegelka, Stefanie and Song, Le and Szepesvari, Csaba and Niu, Gang and Sabato, Sivan},
  volume = 	 {162},
  series = 	 {Proceedings of Machine Learning Research},
  month = 	 {17--23 Jul},
  publisher =    {PMLR},
  pdf = 	 {https://proceedings.mlr.press/v162/schaeffer22a/schaeffer22a.pdf},
  url = 	 {https://proceedings.mlr.press/v162/schaeffer22a.html},
  abstract = 	 {Unsupervised learning from a continuous stream of data is arguably one of the most common and most challenging problems facing intelligent agents. One class of unsupervised models, collectively termed