Fetching the paper…
Reading the bibliography…
The notion of uncertainty is of major importance in machine learning and constitutes a key element of machine learning methodology.
1903
Earlier work this paper cites.
1906
Earlier work this paper cites.
1906
Earlier work this paper cites.
Ziyin L, Wang Z, Liang PP, Salakhutdinov R, Morency LP, Ueda M (2019) Deep gamblers: Learning to abstain with portfolio theory. 1907.00208
1907
Earlier work this paper cites.
Hartley R (1928) Transmission of information. Bell Syst Tech Journal 7(3):535–563
1928
Earlier work this paper cites.
Jeffreys H (1946) An invariant form for the prior probability in estimation problems. Proceedings of the Royal Society A 186:453–461
1946
Earlier work this paper cites.
Kolmogorov A (1965) Three approaches to the quantitative definition of information. Problems Inform Trans 1(1):1–7
1965
Earlier work this paper cites.
Chow C (1970) On optimum recognition error and reject tradeoff. IEEE
1970
Earlier work this paper cites.
Hellman M (1970) The nearest neighbor classification rule with a reject option. IEEE Transactions on Systems, Man and Cybernetics SMC-6:179–185
1970
Earlier work this paper cites.
Rényi A (1970) Probability Theory. North-Holland, Amsterdam
1970
Earlier work this paper cites.
Shilkret N (1971) Maxitive measure and integration. Nederl Akad Wetensch Proc Ser A 74 = Indag Math 33:109–116
1971
Earlier work this paper cites.
Sugeno M (1974) Theory of fuzzy integrals and its application. PhD thesis, Tokyo Institute of Technology
1974
Earlier work this paper cites.
Matheron G (1975) Random Sets and Integral Geometry. John Wiley and Sons
1975
Earlier work this paper cites.
Shafer G (1976) A Mathematical Theory of Evidence. Princeton University Press
1976
Earlier work this paper cites.
Mitchell T (1977) Version spaces: A candidate elimination approach to rule learning. In: Proceedings IJCAI-77
1977
Earlier work this paper cites.
Nguyen H (1978) On random sets and belief functions. Journal of Mathematical Analysis and Applications 65:531–542
1978
Earlier work this paper cites.
Bernardo J (1979) Reference posterior distributions for Bayesian inference. Journal of the Royal Statistical Society, Series B (Methodological) 41(2):113–147
1979
Earlier work this paper cites.
Mitchell T (1980) The need for biases in learning generalizations. Tech. Rep. TR CBM–TR–117, Rutgers University
1980
Earlier work this paper cites.
Yager R (1983) Entropy and specificity in a mathematical theory of evidence. International Journal of General Systems 9:249–260
1983
Earlier work this paper cites.
Klir G, Mariano M (1987) On the uniqueness of possibilistic measure of uncertainty and information. Fuzzy Sets and Systems 24(2):197–219
1987
Earlier work this paper cites.
Dubois D, Prade H (1988) Possibility Theory. Plenum Press
1988
Earlier work this paper cites.
Wasserman L (1990) Belief functions and statistical evidence. The Canadian Journal of Statistics 18(3):183–196
1990
Earlier work this paper cites.
Denker J, LeCun Y (1991) Transforming neural-net output levels to probability distributions. In: Proc. NIPS, Advances in Neural Information Processing Systems
1991
Earlier work this paper cites.
Kruse R, Schwecke E, Heinsohn J (1991) Uncertainty and Vagueness in Knowledge Based Systems. Springer-Verlag
1991
Earlier work this paper cites.
Walley P (1991) Statistical Reasoning with Imprecise Probabilities. Chapman and Hall
1991
Earlier work this paper cites.
Kay DM (1992) A practical Bayesian framework for backpropagation networks. NeuralComputation 4(3):448–472
1992
Earlier work this paper cites.
Klir G (1994) Measures of uncertainty in the Dempster-Shafer theory of evidence. In: Yager R, Fedrizzi M, Kacprzyk J (eds) Advances in the Dempster-Shafer theory of evidence, Wiley, New York, pp. 35–49
1994
Earlier work this paper cites.
Smets P, Kennes R (1994) The transferable belief model. Artificial Intelligence 66:191–234
1994
Earlier work this paper cites.
Dubois D, Prade H, Smets P (1996) Representing partial ignorance. IEEE Transactions on Systems, Man and Cybernetics, Series A 26(3):361–377
1996
Earlier work this paper cites.
Hora S (1996) Aleatory and epistemic uncertainty in probability elicitation with an example from hazardous waste management. Reliability Engineering and System Safety 54(2–3):217–223
1996
Earlier work this paper cites.
Wolpert D (1996) The lack of a priori distinctions between learning algorithms. Neural Computation 8(7):1341–1390
1996
Earlier work this paper cites.
Dubois D, Moral S, Prade H (1997) A semantics for possibility theory based on likelihoods. Journal of Mathematical Analysis and Applications 205(2):359–380
1997
Earlier work this paper cites.
Vapnik V (1998) Statistical Learning Theory. John Wiley & Sons
1998
Earlier work this paper cites.
Jordan M, Ghahramani Z, Jaakkola T, Saul L (1999) An introduction to variational methods for graphical models. Machine Learning 37(2):183–233
1999
Earlier work this paper cites.
Platt J (1999) Probabilistic outputs for support vector machines and comparison to regularized likelihood methods. In: Smola A, Bartlett P, Schoelkopf B, Schuurmans D (eds) Advances in Large Margin Classifiers, MIT Press, Cambridge, MA, pp. 61–74
1999
Earlier work this paper cites.
Abellan J, Moral S (2000) A non-specificity measure for convex sets of probability distributions. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 8:357–367
2000
Earlier work this paper cites.
Cozman F (2000) Credal networks. Artificial Intelligence 120(2):199–233
2000
Earlier work this paper cites.
Breiman L (2001) Random forests. Machine Learning 45(1):5–32
2001
Earlier work this paper cites.
Zadrozny B, Elkan C (2001) Obtaining calibrated probability estimates from decision trees and Naive Bayesian classifiers. In: Proc. ICML, Int. Conference on Machine Learning, pp. 609–616
2001
Earlier work this paper cites.
Gammerman A, Vovk V (2002) Prediction algorithms and confidence measures based on algorithmic randomness theory. Theoretical Computer Science 287:209–217
2002
Cited alongside, same era.
Klement E, Mesiar R, Pap E (2002) Triangular Norms. Kluwer Academic Publishers
2002
Cited alongside, same era.
Zadrozny B, Elkan C (2002) Transforming classifier scores into accurate multiclass probability estimates. In: Proc. KDD–02, 8th International Conference on Knowledge Discovery and Data Mining, Edmonton, Alberta, Canada, pp. 694–699
2002
Cited alongside, same era.
Endres D, Schindelin J (2003) A new metric for probability distributions. IEEE Transactions on Information Theory 49(7):1858–1860
2003
Cited alongside, same era.
Vovk V, Gammerman A, Shafer G (2003) Algorithmic Learning in a Random World. Springer-Verlag
2003
Cited alongside, same era.
Denoeux T (2014) Likelihood-based belief function: Justification and some extensions to low-quality data. International Journal of Approximate Reasoning 55(7):1535–1547
2014
Later among the works it cites.
Khan SS, Madden MG (2014) One-class classification: taxonomy of study and review of techniques. The Knowledge Engineering Review 29(3):345–374, URL http://dx.doi.org/10.1017/S026988891300043X
2014
Later among the works it cites.
Kruppa J, Liu Y, Biau G, Kohler M, König I, Malley J, Ziegler A (2014) Probability estimation with machine learning methods for dichotomous and multi-category outcome: Theory. Biometrical Journal 56(4):534–563
2014
Later among the works it cites.
Kull M, Flach P (2014) Reliability maps: A tool to enhance probability estimates and improve classification accuracy. In: Proc. ECML/PKDD, European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, Nancy, France, pp. 18–33
2014
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Frieden B (2004) Science from Fisher Information: A Unification. Cambridge University Press
2004
Cited alongside, same era.
Seeger M (2004) Gaussian processes for machine learning. International Journal of Neural Systems 14(2):69–104
2004
Cited alongside, same era.
Tax DM, Duin RP (2004) Support vector data description. Machine Learning 54(1):45–66, DOI 10.1023/B:MACH.0000008084.60811.49, URL https://doi.org/10.1023/B:MACH.0000008084.60811.49
2004
Cited alongside, same era.
Bernardo J (2005) An introduction to the imprecise Dirichlet model for multinomial data. International Journal of Approximate Reasoning 39(2–3):123–150
2005
Cited alongside, same era.
Cattaneo M (2005) Likelihood-based statistical decisions. In: Proc. 4th Int. Symposium on Imprecise Probabilities and their Applications, pp. 107–116
2005
Cited alongside, same era.
Gneiting T, Raftery A (2005) Strictly proper scoring rules, prediction, and estimation. Tech. Rep. 463R, Department of Statistics, University of Washington
2005
Cited alongside, same era.
Abellan J, Klir J, Moral S (2006) Disaggregated total uncertainty measure for credal sets. International Journal of General Systems 35(1)
2006
Cited alongside, same era.
Senge R, Bösner S, Dembczynski K, Haasenritter J, Hirsch O, Donner-Banzhoff N, Hüllermeier E (2014) Reliable classification: Learning classifiers that distinguish aleatoric and epistemic uncertainty. Information Sciences 255:16–29
2014
Later among the works it cites.
Bi W, Kwok J (2015) Bayes-optimal hierarchical multilabel classification. IEEE Transactions on Knowledge and Data Engineering 27:1–1, DOI 10.1109/TKDE.2015.2441707
2015
Later among the works it cites.
2015
Later among the works it cites.
Gal Y, Ghahramani Z (2016) Bayesian convolutional neural networks with Bernoulli approximate variational inference. In: Proc. of the ICLR Workshop Track
2016
Later among the works it cites.
Goodfellow I, Bengio Y, Courville A (2016) Deep Learning. Goodfellow, I., Bengio, Y., Courville, A.: Deep Learning. The MIT Press, Cambridge, Massachusetts, London, England
2016
Later among the works it cites.
Linusson H, Johansson U, Boström H, Löfström T (2016) Reliable confidence predictions using conformal prediction. In: Proc. PAKDD, 20th Pacific-Asia Conference on Knowledge Discovery and Data Mining, Auckland, New Zealand
2016
Later among the works it cites.
Perello-Nieto M, Filho TS, Kull M, Flach P (2016) Background check: A general technique to build more reliable and versatile classifiers. In: Proc. ICDM, International Conference on Data Mining
2016
Later among the works it cites.
Varshney K (2016) Engineering safety in machine learning. In: Proc. Inf. Theory Appl. Workshop, La Jolla, CA
2016
Later among the works it cites.
2016
Later among the works it cites.
Flach P (2017) Classifier calibration. In: Encyclopedia of Machine Learning and Data Mining, Springer, pp. 210–217
2017
Later among the works it cites.
Hendrycks D, Gimpel K (2017) A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: Proc. ICLR, Int. Conference on Learning Representations
2017
Later among the works it cites.
Kendall A, Gal Y (2017) What uncertainties do we need in Bayesian deep learning for computer vision? In: Proc. NIPS, Advances in Neural Information Processing Systems, pp. 5574–5584
2017
Later among the works it cites.
Kull M, de Menezes T, Filho S, Flach P (2017) Beta calibration: a well-founded and easily implemented improvement on logistic calibration for binary classifiers. In: Proc. AISTATS, 20th International Conference on Artificial Intelligence and Statistics, Fort Lauderdale, FL, USA, pp. 623–631
2017
Later among the works it cites.
Lakshminarayanan B, Pritzel A, C. Blundell (2017) Simple and scalable predictive uncertainty estimation using deep ensembles. In: Proc. NeurIPS, 31st Conference on Neural Information Processing Systems, Long Beach, California, USA
2017
Later among the works it cites.
Maau DD, Cozman F, Conaty D, de Campos CP (2017) Credal sum-product networks. In: PMLR: Proceedings of Machine Learning Research (ISIPTA 2017), vol 62, pp. 205–216
2017
Later among the works it cites.
Oh S (2017) Top-k hierarchical classification. In: AAAI, AAAI Press, pp. 2450–2456
2017
Later among the works it cites.
Rangwala H, Naik A (2017) Large scale hierarchical classification: foundations, algorithms and applications. In: The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases
2017
Later among the works it cites.
Depeweg S, Hernandez-Lobato J, Doshi-Velez F, Udluft S (2018) Decomposition of uncertainty in Bayesian deep learning for efficient and risk-sensitive learning. In: Proc. ICML, 35th International Conference on Machine Learning, Stockholm, Sweden
2018
Later among the works it cites.
2018
Later among the works it cites.
Johansson U, Löfström T, Sundell H, Linusson H, Gidenstam A, Boström H (2018) Venn predictors for well-calibrated probability estimation trees. In: Proc. COPA, 7th Symposium on Conformal and Probabilistic Prediction and Applications, Maastricht, The Netherlands, pp. 3–14
2018
Later among the works it cites.
Liang S, Li Y, R. Srikant (2018) Enhancing the reliability of out-of-distribution image detection in neural networks. In: Proc. ICLR, Int. Conference on Learning Representations
2018
Later among the works it cites.
Linusson H, Johansson U, Boström H, Löfström T (2018) Classification with reject option using conformal prediction. In: Proc. PAKDD, 22nd Pacific-Asia Conference on Knowledge Discovery and Data Mining, Melbourne, VIC, Australia
2018
Later among the works it cites.
Malinin A, Gales M (2018) Predictive uncertainty estimation via prior networks. In: Proc. NeurIPS, 32nd Conference on Neural Information Processing Systems, Montreal, Canada
2018
Later among the works it cites.
Nguyen V, Destercke S, Masson M, Hüllermeier E (2018) Reliable multi-class classification based on pairwise epistemic and aleatoric uncertainty. In: Proceedings IJCAI 2018, 27th International Joint Conference on Artificial Intelligence, Stockholm, Sweden, pp. 5089–5095
2018
Later among the works it cites.
2018
Later among the works it cites.
Sato M, Suzuki J, Shindo H, Matsumoto Y (2018) Interpretable adversarial perturbation in input embedding space for text. In: Proceedings IJCAI 2018, 27th International Joint Conference on Artificial Intelligence, Stockholm, Sweden, pp. 4323–4330
2018
Later among the works it cites.
Sensoy M, Kaplan L, Kandemir M (2018) Evidential deep learning to quantify classification uncertainty. In: Proc. NeurIPS, 32nd Conference on Neural Information Processing Systems, Montreal, Canada
2018
Later among the works it cites.
Sourati J, Akcakaya M, Erdogmus D, Leen T, Dy J (2018) A probabilistic active learning algorithm based on Fisher information ratio. IEEE Transactions on Pattern Analysis and Machine Intelligence 40(8)
2018
Later among the works it cites.
Bazargami M, Mac-Namee B (2019) The elliptical basis function data descriptor network: A one-class classification approach for anomaly detection. In: European Conference on Machine Learning and Knowledge Discovery in Databases
2019
Closest in time.
2019
Closest in time.
Nguyen V, Destercke S, Hüllermeier E (2019) Epistemic uncertainty sampling. In: Proc. DS 2019, 22nd International Conference on Discovery Science, Split, Croatia
2019
Closest in time.
Tan M, Le Q (2019) EfficientNet: Rethinking model scaling for convolutional neural networks. In: Proc. ICML, 36th Int. Conference on Machine Learning, Long Beach, California
2019
Closest in time.
Lassiter D (2020) Representing credal imprecision: from sets of measures to hierarchical Bayesian models. Philosophical Studies Forthcoming
2020
Closest in time.
Shaker M, Hüllermeier E (2020) Aleatoric and epistemic uncertainty with random forests. In: Proc. IDA 2020, 18th International Symposium on Intelligent Data Analysis, Springer, Konstanz, Germany, LNCS, vol 12080, pp. 444–456, DOI 10.1007/978-3-030-44584-3_35
2020
Closest in time.