Epistemic Uncertainty Sampling
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
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
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
No references could be resolved yet.
Research works printed but not linked: 19
- 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.
- Allan Birnbaum. On the foundations of statistical inference. Journal of the American Statistical Association, 57(298):269-306, 1962.
- Léon Bottou and Vladimir Vapnik. Local learning algorithms. Neural Computation, 4(6):888-900, 1992.
- Olivier Chapelle. Active learning for parzen window classifier. In Proceedings of the Tenth International Workshop on Artificial Intelligence and Statistics (AISTATS), volume 5, pages 49-56, 2005.
- Thomas Cover and Peter Hart. Nearest neighbor pattern classification. IEEE Trans- actions on Information Theory, 13(1):21-27, 1967.
- Yifan Fu, Xingquan Zhu, and Bin Li. A survey on instance selection for active learning. Knowledge and Information Systems, pages 1-35, 2013.
- Trevor Hastie, Robert Tibshirani, Jerome Friedman, and James Franklin. The ele- ments of statistical learning: Data mining, inference and prediction. The Mathemat- ical Intelligencer, 27(2):83-85, 2005.
- 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.
- 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.
- Upmanu Lall and Ashish Sharma. A nearest neighbor bootstrap for resampling hy- drologic time series. Water Resources Research, 32(3):679-693, 1996.
- 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.
- 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.
- Jorge Nocedal and S Wright. Numerical Optimization. Springer New York, 2006.
- J. Ross Quinlan. Induction of decision trees. Machine learning, 1(1):81-106, 1986.
- S Rasoul Safavian and David Landgrebe. A survey of decision tree classifier method- ology. IEEE transactions on systems, man, and cybernetics, 21(3):660-674, 1991.
- Burr Settles. Active learning literature survey. Technical Report, University of Wis- consin, Madison, 52(55-66):11, 2010.
- Manali Sharma and Mustafa Bilgic. Evidence-based uncertainty sampling for active learning. Data Mining and Knowledge Discovery, 31(1):164-202, 2017.
- Vladimir N Vapnik. An overview of statistical learning theory. IEEE Transactions on Neural Networks, 10(5):988-999, 1999.
- 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.
Cited by
Sorted by citations
A review of uncertainty quantification in deep learning: Techniques, applications and challenges
Abdar, M., Pourpanah, F., Hussain, S., Rezazadegan, D., Liu, L., Ghavamzadeh, M., Fieguth, P., Cao, X., Khosravi, A., Acharya, U. R., et al. (2021) · Information Fusion · doi:10.1016/j.inffus.2021.05.008 · Cited by 2,720 · see citing works
“learning algorithms [3], and active learning (AL) [4], [5].”
BackgroundAleatoric and epistemic uncertainty in machine learning: an introduction to concepts and methods
Hüllermeier, E., & Waegeman, W. (2021) · Machine Learning · doi:10.1007/s10994-021-05946-3 · Cited by 1,834 · see citing works
Self-citationA Survey of Uncertainty in Deep Neural Networks
Gawlikowski, J., Tassi, C. R. N., Ali, M., Lee, J., Humt, M., Feng, J., Kruspe, A., Triebel, R., Jung, P., Roscher, R., et al. (2021) · arXiv · doi:10.48550/arxiv.2107.03342 · Cited by 232 · see citing works
Machine learning with a reject option: A survey
Hendrickx, K., Perini, L., Van der Plas, D., Meert, W., & Davis, J. (2024) · Machine Learning · doi:10.1007/s10994-024-06534-x · Cited by 134 · see citing works
Decomposing LLM-Judge Uncertainty to Target Expert Labels
Lail, R. (2026) · arXiv · doi:10.48550/arxiv.2609.06444
- Authors
- Nguyen, V.-L., Destercke, S., & Hüllermeier, E.
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
- Published
- 2019
- Pages
- 72-86
- ISSN
- 0302-9743
- 1611-3349
- Cited by
- 31
- References
- 0
- Language
- en
- Licence
- www.springer.com/tdm (tdm)
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review