Print Email Facebook Twitter Online label aggregation Title Online label aggregation: A variational bayesian approach Author Hong, C. (TU Delft Data-Intensive Systems) Ghiassi, S. (TU Delft Data-Intensive Systems) Zhou, Yichi (Tsinghua University) Birke, Robert (ABB Switzerland Ltd.) Chen, Lydia Y. (TU Delft Data-Intensive Systems) Date 2021 Abstract Noisy labeled data is more a norm than a rarity for crowd sourced contents. It is effective to distill noise and infer correct labels through aggregating results from crowd workers. To ensure the time relevance and overcome slow responses of workers, online label aggregation is increasingly requested, calling for solutions that can incrementally infer true label distribution via subsets of data items. In this paper, we propose a novel online label aggregation framework, BiLA , which employs variational Bayesian inference method and designs a novel stochastic optimization scheme for incremental training. BiLA is flexible to accommodate any generating distribution of labels by the exact computation of its posterior distribution. We also derive the convergence bound of the proposed optimizer. We compare BiLA with the state of the art based on minimax entropy, neural networks and expectation maximization algorithms, on synthetic and real-world data sets. Our evaluation results on various online scenarios show that BiLA can effectively infer the true labels, with an error rate reduction of at least 10 to 1.5 percent points for synthetic and real-world datasets, respectively. Subject Convergence boundLabel aggregationOnlineStochastic optimizerVariational bayesian inference To reference this document use: http://resolver.tudelft.nl/uuid:3e5ee25c-a945-471c-bb4f-f8ec459687de DOI https://doi.org/10.1145/3442381.3449933 Publisher Association for Computing Machinery (ACM) ISBN 978-1-4503-8312-7 Source The Web Conference 2021 - Proceedings of the World Wide Web Conference, WWW 2021 Event 2021 World Wide Web Conference, WWW 2021, 2021-04-19 → 2021-04-23, Ljubljana, Slovenia Series The Web Conference 2021 - Proceedings of the World Wide Web Conference, WWW 2021 Part of collection Institutional Repository Document type conference paper Rights © 2021 C. Hong, S. Ghiassi, Yichi Zhou, Robert Birke, Lydia Y. Chen Files PDF 3442381.3449933.pdf 1.64 MB Close viewer /islandora/object/uuid:3e5ee25c-a945-471c-bb4f-f8ec459687de/datastream/OBJ/view