Cited by 31

Epistemic Uncertainty Sampling

Nguyen, Vu-Linh, Destercke, Sébastien, Hüllermeier, Eyke·2019·Lecture Notes in Computer Science

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Crossref · 10.1007/978-3-030-33778-0_7

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  • The epistemic term I_x is BALD's acquisition score.

    “The epistemic term I x is BALD's acquisition score [Houlsby et al., 2011]”

    From Decomposing LLM-Judge Uncertainty to Target Expert Labels (2026) · cites this paper as Nguyen et al., 2019

    Section 3 defines this paper’s epistemic uncertainty measure as u_e(x) and uses it to select instances, but does not identify an I_x term or BALD’s acquisition score. Searches for BALD, Houlsby, and information gain found no passage in our copy addressing that identification. The printed sentence cites Houlsby et al. (2011), not the supplied Nguyen et al. paper. (LLM, conf=0.76)

    Experimental — accuracy not yet measured

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Research works printed but not linked: 19

  1. Alessandro Antonucci, Giorgio Corani, and Sandra Gabaglio. Active learning by the naive credal classifier. In Proceedings of the Sixth European Workshop on Probabilistic Graphical Models (PGM), pages 3-10, 2012.
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  6. Yifan Fu, Xingquan Zhu, and Bin Li. A survey on instance selection for active learning. Knowledge and Information Systems, pages 1-35, 2013.
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  8. Stephen C Hora. Aleatory and epistemic uncertainty in probability elicitation with an example from hazardous waste management. Reliability Engineering & System Safety, 54(2-3):217-223, 1996.
  9. A. Kendall and Y. Gal. What uncertainties do we need in bayesian deep learning for computer vision? In Proceedings of the 31st Conference on Neural Information Processing Systems (NIPS), 2017.
  10. Upmanu Lall and Ashish Sharma. A nearest neighbor bootstrap for resampling hy- drologic time series. Water Resources Research, 32(3):679-693, 1996.
  11. David D Lewis and William A Gale. A sequential algorithm for training text classi- fiers. In Proceedings of the 17th annual International SIGIR Conference on Research and Development in Information Retrieval, pages 3-12. Springer, 1994.
  12. Vu-Linh Nguyen, Sébastien Destercke, Marie-Hélène Masson, and Eyke Hüllermeier. Reliable multi-class classification based on pairwise epistemic and aleatoric uncer- tainty. In Proceedings of the 27th International Joint Conference on Artificial Intel- ligence (IJCAI), pages 5089-5095. AAAI Press, 2018.
  13. Jorge Nocedal and S Wright. Numerical Optimization. Springer New York, 2006.
  14. J. Ross Quinlan. Induction of decision trees. Machine learning, 1(1):81-106, 1986.
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  16. Burr Settles. Active learning literature survey. Technical Report, University of Wis- consin, Madison, 52(55-66):11, 2010.
  17. Manali Sharma and Mustafa Bilgic. Evidence-based uncertainty sampling for active learning. Data Mining and Knowledge Discovery, 31(1):164-202, 2017.
  18. Vladimir N Vapnik. An overview of statistical learning theory. IEEE Transactions on Neural Networks, 10(5):988-999, 1999.
  19. Peter Walley and Serafin Moral. Upper probabilities based only on the likelihood function. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 61(4):831-847, 1999.

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Authors
Nguyen, V.-L., Destercke, S., & Hüllermeier, E.
Venue
Lecture Notes in Computer Science
Published
2019
Pages
72-86
ISSN
  • 0302-9743
  • 1611-3349
Cited by
31
References
0
Language
en

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