Evaluating Predictive Uncertainty Challenge
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
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?
Negative log-likelihood is a proper scoring rule but can over-emphasize tail probabilities.
“Although a proper scoring rule (Gneiting & Raftery, 2007), it can over-emphasize tail probabilities (Quinonero-Candela et al., 2006).”
From Can You Trust Your Model's Uncertainty? Evaluating Predictive Uncertainty Under Dataset Shift (2019) · cites this paper as Quinonero-Candela et al., 2006
Cited by
Sorted by citations
Strictly Proper Scoring Rules, Prediction, and Estimation
Gneiting, T., & Raftery, A. E. (2007) · Journal of the American Statistical Association · doi:10.1198/016214506000001437 · Cited by 5,793 · see citing works
AUC: A misleading measure of the performance of predictive distribution models
Lobo, J. M., Jiménez‐Valverde, A., & Real, R. (2008) · Global Ecology and Biogeography · doi:10.1111/j.1466-8238.2007.00358.x · Cited by 3,499 · see citing works
Can You Trust Your Model's Uncertainty? Evaluating Predictive Uncertainty Under Dataset Shift
Ovadia, Y., Fertig, E., Ren, J., Nado, Z., Sculley, D., Nowozin, S., Dillon, J. V., Lakshminarayanan, B., & Snoek, J. (2019) · arXiv · doi:10.48550/arxiv.1906.02530 · Cited by 989 · see citing works
Zhu, Y., & Zabaras, N. (2018) · Journal of Computational Physics · doi:10.1016/j.jcp.2018.04.018 · Cited by 704 · see citing works
The benefit of the doubt: Uncertainty aware sensing for edge computing platforms
Qendro, L., Chauhan, J., Ramos, A. G. C. P., & Mascolo, C. (2021) · arXiv · doi:10.48550/arxiv.2102.05956 · Cited by 6 · see citing works
- Authors
- Quiñonero-Candela, J., Rasmussen, C. E., Sinz, F., Bousquet, O., & Schölkopf, B.
- Affiliations
- Max Planck Institute for Biological Cybernetics (DE)
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer Berlin Heidelberg
- Published
- 2006
- Pages
- 1-27
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W2158471180
- Cited by
- 116
- References
- 0
- Citations by year
- 2026: 4 · 2025: 5 · 2024: 6 · 2023: 9 · 2022: 15 · 2021: 14 · 2020: 12 · 2019: 11 · 2018: 8 · 2017: 6 · 2016: 5 · 2015: 2 · 2014: 1 · 2013: 1 · 2012: 2
- FWCI
- 2.16
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- Lecture notes in computer science (publishedVersion)
- Publikationsdatenbank der Fraunhofer-Gesellschaft (Fraunhofer-Gesellschaft) (submittedVersion)
- Cambridge University Engineering Department Publications Database (submittedVersion)
- Fraunhofer-Publica (Fraunhofer-Gesellschaft) (submittedVersion)
- MPG.PuRe (Max Planck Society) (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Adversarial Robustness in Machine Learning
- Physical Sciences › Computer Science › Artificial Intelligence › Gaussian Processes and Bayesian Inference
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Data Classification
- Keywords
- probabilistic forecasting, forecast uncertainty, uncertainty quantification, machine learning competition, regression analysis, classification
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 ›
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