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