---
_id: '14201'
abstract:
- lang: eng
  text: "Variational inference is a popular technique to approximate a possibly\r\nintractable
    Bayesian posterior with a more tractable one. Recently, boosting\r\nvariational
    inference has been proposed as a new paradigm to approximate the\r\nposterior
    by a mixture of densities by greedily adding components to the\r\nmixture. However,
    as is the case with many other variational inference\r\nalgorithms, its theoretical
    properties have not been studied. In the present\r\nwork, we study the convergence
    properties of this approach from a modern\r\noptimization viewpoint by establishing
    connections to the classic Frank-Wolfe\r\nalgorithm. Our analyses yields novel
    theoretical insights regarding the\r\nsufficient conditions for convergence, explicit
    rates, and algorithmic\r\nsimplifications. Since a lot of focus in previous works
    for variational\r\ninference has been on tractability, our work is especially
    important as a much\r\nneeded attempt to bridge the gap between probabilistic
    models and their\r\ncorresponding theoretical properties."
alternative_title:
- PMLR
article_processing_charge: No
arxiv: 1
author:
- first_name: Francesco
  full_name: Locatello, Francesco
  id: 26cfd52f-2483-11ee-8040-88983bcc06d4
  last_name: Locatello
  orcid: 0000-0002-4850-0683
- first_name: Rajiv
  full_name: Khanna, Rajiv
  last_name: Khanna
- first_name: Joydeep
  full_name: Ghosh, Joydeep
  last_name: Ghosh
- first_name: Gunnar
  full_name: Rätsch, Gunnar
  last_name: Rätsch
citation:
  ama: 'Locatello F, Khanna R, Ghosh J, Rätsch G. Boosting variational inference:
    An optimization perspective. In: <i>Proceedings of the 21st International Conference
    on Artificial Intelligence and Statistics</i>. Vol 84. ML Research Press; 2018:464-472.'
  apa: 'Locatello, F., Khanna, R., Ghosh, J., &#38; Rätsch, G. (2018). Boosting variational
    inference: An optimization perspective. In <i>Proceedings of the 21st International
    Conference on Artificial Intelligence and Statistics</i> (Vol. 84, pp. 464–472).
    Playa Blanca, Lanzarote: ML Research Press.'
  chicago: 'Locatello, Francesco, Rajiv Khanna, Joydeep Ghosh, and Gunnar Rätsch.
    “Boosting Variational Inference: An Optimization Perspective.” In <i>Proceedings
    of the 21st International Conference on Artificial Intelligence and Statistics</i>,
    84:464–72. ML Research Press, 2018.'
  ieee: 'F. Locatello, R. Khanna, J. Ghosh, and G. Rätsch, “Boosting variational inference:
    An optimization perspective,” in <i>Proceedings of the 21st International Conference
    on Artificial Intelligence and Statistics</i>, Playa Blanca, Lanzarote, 2018,
    vol. 84, pp. 464–472.'
  ista: 'Locatello F, Khanna R, Ghosh J, Rätsch G. 2018. Boosting variational inference:
    An optimization perspective. Proceedings of the 21st International Conference
    on Artificial Intelligence and Statistics. AISTATS: Conference on Artificial Intelligence
    and Statistics, PMLR, vol. 84, 464–472.'
  mla: 'Locatello, Francesco, et al. “Boosting Variational Inference: An Optimization
    Perspective.” <i>Proceedings of the 21st International Conference on Artificial
    Intelligence and Statistics</i>, vol. 84, ML Research Press, 2018, pp. 464–72.'
  short: F. Locatello, R. Khanna, J. Ghosh, G. Rätsch, in:, Proceedings of the 21st
    International Conference on Artificial Intelligence and Statistics, ML Research
    Press, 2018, pp. 464–472.
conference:
  end_date: 2018-04-11
  location: Playa Blanca, Lanzarote
  name: 'AISTATS: Conference on Artificial Intelligence and Statistics'
  start_date: 2018-04-09
date_created: 2023-08-22T14:15:20Z
date_published: 2018-04-15T00:00:00Z
date_updated: 2024-10-14T12:29:27Z
day: '15'
department:
- _id: FrLo
extern: '1'
external_id:
  arxiv:
  - '1708.01733'
intvolume: '        84'
language:
- iso: eng
main_file_link:
- open_access: '1'
  url: https://arxiv.org/abs/1708.01733
month: '04'
oa: 1
oa_version: Preprint
page: 464-472
publication: Proceedings of the 21st International Conference on Artificial Intelligence
  and Statistics
publication_status: published
publisher: ML Research Press
quality_controlled: '1'
scopus_import: '1'
status: public
title: 'Boosting variational inference: An optimization perspective'
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 84
year: '2018'
...