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Uncertainty decomposition refers to the task of decomposing the total uncertainty of a predictive model into aleatoric (data) uncertainty, resulting from inherent randomness in the data-generating process, and epistemic (model) uncertainty, resulting from missing information in the model's training data.
Regular tree grammars as a formalism for scope underspecification
Koller, A., Regneri, M., and Thater, S · 2008
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Practical variational inference for neural networks
Graves, A · 2011
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Bayesian learning for neural networks , volume 118
Neal, R. M · 2012
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Weight uncertainty in neural network
Blundell, C., Cornebise, J., Kavukcuoglu, K., and Wierstra, D · 2015
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Probabilistic backpropagation for scalable learning of bayesian neural networks
Hernández-Lobato, J. M. and Adams, R · 2015
Earlier work this paper cites.
Stochastic expectation propagation
Li, Y., Hernández-Lobato, J. M., and Turner, R. E · 2015
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Dropout as a bayesian approximation: Representing model uncertainty in deep learning
Gal, Y. and Ghahramani, Z · 2016
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Uncertainty in deep learning, 2016
Gal, Y. et al · 2016
Earlier work this paper cites.
A baseline for detecting misclassified and out-of-distribution examples in neural networks
Hendrycks, D. and Gimpel, K · 2016
Earlier work this paper cites.
Structured and efficient variational deep learning with matrix gaussian posteriors
Louizos, C. and Welling, M · 2016
Earlier work this paper cites.
On calibration of modern neural networks
Guo, C., Pleiss, G., Sun, Y., and Weinberger, K. Q · 2017
Earlier work this paper cites.
Distributed bayesian learning with stochastic natural gradient expectation propagation and the posterior server
Hasenclever, L., Webb, S., Lienart, T., Vollmer, S., Lakshminarayanan, B., Blundell, C., and Teh, Y. W · 2017
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Simple and scalable predictive uncertainty estimation using deep ensembles
Lakshminarayanan, B., Pritzel, A., and Blundell, C · 2017
Earlier work this paper cites.
Predictive uncertainty estimation via prior networks
Malinin, A. and Gales, M · 2018
Earlier work this paper cites.
Analyzing uncertainty in neural machine translation
Ott, M., Auli, M., Grangier, D., and Ranzato, M · 2018
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Deep bayesian bandits showdown: An empirical comparison of bayesian deep networks for thompson sampling
Riquelme, C., Tucker, G., and Snoek, J · 2018
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Bayesian uncertainty estimation for batch normalized deep networks
Teye, M., Azizpour, H., and Smith, K · 2018
Earlier work this paper cites.
Deep ensembles: A loss landscape perspective
Fort, S., Hu, H., and Lakshminarayanan, B · 2019
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Natural questions: a benchmark for question answering research
Kwiatkowski, T., Palomaki, J., Redfield, O., Collins, M., Parikh, A., Alberti, C., Epstein, D., Polosukhin, I., Devlin, J., Lee, K., et al · 2019
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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., Lakshminarayanan, B., and Snoek, J · 2019
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Sentence-bert: Sentence embeddings using siamese bert-networks
Reimers, N. and Gurevych, I · 2019
Cited alongside, same era.
Calibration of pre-trained transformers
Desai, S. and Durrett, G · 2020
Cited alongside, same era.
Bayesian deep ensembles via the neural tangent kernel
He, B., Lakshminarayanan, B., and Teh, Y. W · 2020
Cited alongside, same era.
Uncertainty estimation in autoregressive structured prediction
Malinin, A. and Gales, M · 2020
Cited alongside, same era.
Uncertainty in gradient boosting via ensembles
Malinin, A., Prokhorenkova, L., and Ustimenko, A · 2020
Cited alongside, same era.
Ambigqa: Answering ambiguous open-domain questions
Min, S., Michael, J., Hajishirzi, H., and Zettlemoyer, L · 2020
Cited alongside, same era.
Prompting gpt-3 to be reliable
Si, C., Gan, Z., Yang, Z., Wang, S., Wang, J., Boyd-Graber, J. L., and Wang, L · 2022
Later among the works it cites.
Task ambiguity in humans and language models
Tamkin, A., Handa, K., Shrestha, A., and Goodman, N · 2022
Later among the works it cites.
Self-consistency improves chain of thought reasoning in language models
Wang, X., Wei, J., Schuurmans, D., Le, Q. V., Chi, E. H., Narang, S., Chowdhery, A., and Zhou, D · 2022
Later among the works it cites.
Uncertainty quantification with pre-trained language models: A large-scale empirical analysis
Xiao, Y., Liang, P. P., Bhatt, U., Neiswanger, W., Salakhutdinov, R., and Morency, L.-P · 2022
Later among the works it cites.
Can explanations be useful for calibrating black box models?, 2022
Ye, X. and Durrett, G · 2022
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Uncertainty as a form of transparency: Measuring, communicating, and using uncertainty
Bhatt, U., Antorán, J., Zhang, Y., Liao, Q. V., Sattigeri, P., Fogliato, R., Melançon, G., Krishnan, R., Stanley, J., Tickoo, O., et al · 2021
Cited alongside, same era.
Training verifiers to solve math word problems
Cobbe, K., Kosaraju, V., Bavarian, M., Chen, M., Jun, H., Kaiser, L., Plappert, M., Tworek, J., Hilton, J., Nakano, R., et al · 2021
Cited alongside, same era.
Abg-coqa: Clarifying ambiguity in conversational question answering
Guo, M., Zhang, M., Reddy, S., and Alikhani, M · 2021
Cited alongside, same era.
Aleatoric and epistemic uncertainty in machine learning: An introduction to concepts and methods
Hüllermeier, E. and Waegeman, W · 2021
Cited alongside, same era.
How can we know when language models know? on the calibration of language models for question answering
Jiang, Z., Araki, J., Ding, H., and Neubig, G · 2021
Cited alongside, same era.
Learnable uncertainty under laplace approximations
Kristiadi, A., Hein, M., and Hennig, P · 2021
Cited alongside, same era.
Chen, J. and Mueller, J · 2023
Closest in time.
Selectively answering ambiguous questions
Cole, J. R., Zhang, M. J., Gillick, D., Eisenschlos, J. M., Dhingra, B., and Eisenstein, J · 2023
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Shifting attention to relevance: Towards the uncertainty estimation of large language models
Duan, J., Cheng, H., Wang, S., Wang, C., Zavalny, A., Xu, R., Kailkhura, B., and Xu, K · 2023
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Look before you leap: An exploratory study of uncertainty measurement for large language models
Huang, Y., Song, J., Wang, Z., Chen, H., and Ma, L · 2023
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Calibrating language models via augmented prompt ensembles
Jiang, M., Ruan, Y., Huang, S., Liao, S., Pitis, S., Grosse, R. B., and Ba, J · 2023
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Clam: Selective clarification for ambiguous questions with generative language models
Kuhn, L., Gal, Y., and Farquhar, S · 2023
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Generating with confidence: Uncertainty quantification for black-box large language models
Lin, Z., Trivedi, S., and Sun, J · 2023
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We’re afraid language models aren’t modeling ambiguity
Liu, A., Wu, Z., Michael, J., Suhr, A., West, P., Koller, A., Swayamdipta, S., Smith, N. A., and Choi, Y · 2023
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Pac neural prediction set learning to quantify the uncertainty of generative language models
Park, S. and Kim, T · 2023
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Robots that ask for help: Uncertainty alignment for large language model planners
Ren, A., Dixit, A., Bodrova, A., Singh, S., Tu, S., Brown, N., Xu, P., Takayama, L., Xia, F., Varley, J., et al · 2023
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Post-hoc uncertainty learning using a dirichlet meta-model
Shen, M., Bu, Y., Sattigeri, P., Ghosh, S., Das, S., and Wornell, G · 2023
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Prompting GPT-3 to be reliable
Si, C., Gan, Z., Yang, Z., Wang, S., Wang, J., Boyd-Graber, J. L., and Wang, L · 2023
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Tian, K., Mitchell, E., Zhou, A., Sharma, A., Rafailov, R., Yao, H., Finn, C., and Manning, C. D · 2023
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Navigating the grey area: Expressions of overconfidence and uncertainty in language models
Zhou, K., Jurafsky, D., and Hashimoto, T · 2023
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