---
res:
  bibo_abstract:
  - 'The labeling process for supervised learning is costly and time-consuming, and
    is often impractical to scale due to real-world constraints. Active learning (AL)
    addresses this challenge by strategically selecting representative and informative
    data points to reduce labeling efforts. This paper focuses on an AL scenario in
    which only a very limited number of labels can be acquired. We propose an algorithm
    operating in two phases: (1) an exploration phase that prioritizes representative
    and diverse data points using density-driven criteria, and (2) an exploitation
    phase that combines predictive uncertainty with density weighting to select informative
    samples from densely populated regions. This enhances both representativeness
    and informativeness. Our results demonstrate significant improvements in model
    quality compared to other algorithms typically employed for this scenario, across
    various scenarios involving imbalanced data in classification tasks and skewness
    in regression tasks. Through this work, we aim to provide a new algorithm for
    this scenario and investigate general principles for AL. While most AL studies
    focus on either classification or regression, our work applies the algorithms
    to both. Therefore, we can analyze the differences between classification and
    regression problems and their effects on AL strategies. Furthermore, we explore
    different categories of AL criteria and their effectiveness in the low-budget
    regime. These results also provide insight into the cold-start problem, which
    involves selecting an initial labeled set and is faced by many model-based AL
    methods.@eng'
  bibo_authorlist:
  - foaf_Person:
      foaf_givenName: Bjarne
      foaf_name: Jaster, Bjarne
      foaf_surname: Jaster
      foaf_workInfoHomepage: http://www.librecat.org/personId=252434
    orcid: 0000-0002-8362-5369
  - foaf_Person:
      foaf_givenName: Alaa
      foaf_name: Tharwat, Alaa
      foaf_surname: Tharwat
      foaf_workInfoHomepage: http://www.librecat.org/personId=238549
  - foaf_Person:
      foaf_givenName: Eiram Mahera
      foaf_name: Sheikh, Eiram Mahera
      foaf_surname: Sheikh
  - foaf_Person:
      foaf_givenName: Martin
      foaf_name: Kohlhase, Martin
      foaf_surname: Kohlhase
      foaf_workInfoHomepage: http://www.librecat.org/personId=226669
    orcid: 0009-0002-9374-0720
  - foaf_Person:
      foaf_givenName: Wolfram
      foaf_name: Schenck, Wolfram
      foaf_surname: Schenck
      foaf_workInfoHomepage: http://www.librecat.org/personId=224375
    orcid: 0000-0003-3300-2048
  bibo_doi: 10.1007/978-3-032-19105-2_1
  dct_date: 2026^xs_gYear
  dct_isPartOf:
  - http://id.crossref.org/issn/1865-0929
  - http://id.crossref.org/issn/1865-0937
  - http://id.crossref.org/issn/978-3-032-19104-5
  dct_language: eng
  dct_publisher: Springer Nature Switzerland@
  dct_title: Low Query Budget Active Learning for Classification and Regression@
...