---
_id: '12161'
abstract:
- lang: eng
  text: 'We introduce LIMES, a new method for learning with non-stationary streaming
    data, inspired by the recent success of meta-learning. The main idea is not to
    attempt to learn a single classifier that would have to work well across all occurring
    data distributions, nor many separate classifiers, but to exploit a hybrid strategy:
    we learn a single set of model parameters from which a specific classifier for
    any specific data distribution is derived via classifier adaptation. Assuming
    a multiclass classification setting with class-prior shift, the adaptation step
    can be performed analytically with only the classifier’s bias terms being affected.
    Another contribution of our work is an extrapolation step that predicts suitable
    adaptation parameters for future time steps based on the previous data. In combination,
    we obtain a lightweight procedure for learning from streaming data with varying
    class distribution that adds no trainable parameters and almost no memory or computational
    overhead compared to training a single model. Experiments on a set of exemplary
    tasks using Twitter data show that LIMES achieves higher accuracy than alternative
    approaches, especially with respect to the relevant real-world metric of lowest
    within-day accuracy.'
article_processing_charge: No
arxiv: 1
author:
- first_name: Paulina
  full_name: Tomaszewska, Paulina
  last_name: Tomaszewska
- first_name: Christoph
  full_name: Lampert, Christoph
  id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
  last_name: Lampert
  orcid: 0000-0001-8622-7887
citation:
  ama: 'Tomaszewska P, Lampert C. Lightweight conditional model extrapolation for
    streaming data under class-prior shift. In: <i>26th International Conference on
    Pattern Recognition</i>. Vol 2022. Institute of Electrical and Electronics Engineers;
    2022:2128-2134. doi:<a href="https://doi.org/10.1109/icpr56361.2022.9956195">10.1109/icpr56361.2022.9956195</a>'
  apa: 'Tomaszewska, P., &#38; Lampert, C. (2022). Lightweight conditional model extrapolation
    for streaming data under class-prior shift. In <i>26th International Conference
    on Pattern Recognition</i> (Vol. 2022, pp. 2128–2134). Montreal, Canada: Institute
    of Electrical and Electronics Engineers. <a href="https://doi.org/10.1109/icpr56361.2022.9956195">https://doi.org/10.1109/icpr56361.2022.9956195</a>'
  chicago: Tomaszewska, Paulina, and Christoph Lampert. “Lightweight Conditional Model
    Extrapolation for Streaming Data under Class-Prior Shift.” In <i>26th International
    Conference on Pattern Recognition</i>, 2022:2128–34. Institute of Electrical and
    Electronics Engineers, 2022. <a href="https://doi.org/10.1109/icpr56361.2022.9956195">https://doi.org/10.1109/icpr56361.2022.9956195</a>.
  ieee: P. Tomaszewska and C. Lampert, “Lightweight conditional model extrapolation
    for streaming data under class-prior shift,” in <i>26th International Conference
    on Pattern Recognition</i>, Montreal, Canada, 2022, vol. 2022, pp. 2128–2134.
  ista: 'Tomaszewska P, Lampert C. 2022. Lightweight conditional model extrapolation
    for streaming data under class-prior shift. 26th International Conference on Pattern
    Recognition. ICPR: International Conference on Pattern Recognition vol. 2022,
    2128–2134.'
  mla: Tomaszewska, Paulina, and Christoph Lampert. “Lightweight Conditional Model
    Extrapolation for Streaming Data under Class-Prior Shift.” <i>26th International
    Conference on Pattern Recognition</i>, vol. 2022, Institute of Electrical and
    Electronics Engineers, 2022, pp. 2128–34, doi:<a href="https://doi.org/10.1109/icpr56361.2022.9956195">10.1109/icpr56361.2022.9956195</a>.
  short: P. Tomaszewska, C. Lampert, in:, 26th International Conference on Pattern
    Recognition, Institute of Electrical and Electronics Engineers, 2022, pp. 2128–2134.
conference:
  end_date: 2022-08-25
  location: Montreal, Canada
  name: 'ICPR: International Conference on Pattern Recognition'
  start_date: 2022-08-21
corr_author: '1'
date_created: 2023-01-12T12:09:38Z
date_published: 2022-11-29T00:00:00Z
date_updated: 2024-10-09T21:03:41Z
day: '29'
department:
- _id: ChLa
doi: 10.1109/icpr56361.2022.9956195
external_id:
  arxiv:
  - '2206.05181'
  isi:
  - '000897707602018'
intvolume: '      2022'
isi: 1
language:
- iso: eng
main_file_link:
- open_access: '1'
  url: https://doi.org/10.48550/arXiv.2206.05181
month: '11'
oa: 1
oa_version: Preprint
page: 2128-2134
publication: 26th International Conference on Pattern Recognition
publication_identifier:
  eisbn:
  - '9781665490627'
  eissn:
  - 2831-7475
publication_status: published
publisher: Institute of Electrical and Electronics Engineers
quality_controlled: '1'
scopus_import: '1'
status: public
title: Lightweight conditional model extrapolation for streaming data under class-prior
  shift
type: conference
user_id: 4359f0d1-fa6c-11eb-b949-802e58b17ae8
volume: 2022
year: '2022'
...