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Cited by 401Markov Chains and Monte Carlo MethodsGaussian Processes and Bayesian InferenceTarget Tracking and Data Fusion in Sensor Networks

Herding dynamical weights to learn

Welling, Max·2009·Proceedings of the 26th Annual International Conference on Machine Learning
DOI 10.1145/1553374.1553517
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Abstract

A new "herding" algorithm is proposed which directly converts observed moments into a sequence of pseudo-samples. The pseudo-samples respect the moment constraints and may be used to estimate (unobserved) quantities of interest. The procedure allows us to sidestep the usual approach of first learning a joint model (which is intractable) and then sampling from that model (which can easily get stuck in a local mode). Moreover, the algorithm is fully deterministic, avoiding random number generation) and does not need expensive operations such as exponentiation.

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Crossref · 10.1145/1553374.1553517

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  • The original definition of herding provides a better approximation of the actual class center compared to a static selection.

    “The advantage of the original definition is that it provides a better approximation of the actual class center compared to a static selection.”

    From A Comprehensive Study of Class Incremental Learning Algorithms for Visual Tasks (2020) · cites this paper as [74]

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Authors
Welling, M.
Affiliations
University of California, Irvine (US)
Corresponding author
Welling, Max
ORCID
Welling, Max
Venue
Proceedings of the 26th Annual International Conference on Machine Learning
Publisher
ACM
Published
2009-06-14
Pages
1121-1128
DOI
10.1145/1553374.1553517
OpenAlex
W1997865285
Cited by
401
References
2
Citations by year
2026: 38 · 2025: 62 · 2024: 70 · 2023: 58 · 2022: 38 · 2021: 39 · 2020: 27 · 2019: 7 · 2018: 4 · 2017: 9 · 2016: 11 · 2015: 6 · 2014: 8 · 2013: 7 · 2012: 8
FWCI
17.51
Type
conference paper
Language
en
Open access
closed
Licence
www.acm.org/publications/policies/copyright_policy#Background (vor)
Locations
International Conference on Machine Learning (publishedVersion)
Funders
  • Division of Information and Intelligent Systems (IIS-0447903IIS-0535278)
  • Office of Naval Research (00014-06-1-073)
Topics
  • Physical Sciences › Mathematics › Statistics and Probability › Markov Chains and Monte Carlo Methods
  • Physical Sciences › Computer Science › Artificial Intelligence › Gaussian Processes and Bayesian Inference
  • Physical Sciences › Computer Science › Artificial Intelligence › Target Tracking and Data Fusion in Sensor Networks
Keywords
moment constraints, pseudo-samples, deterministic sampling, moment matching

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