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- W2613950051 abstract "Distribution regression has recently attracted much interest as a generic solution to the problem of supervised learning where labels are available at the group level, rather than at the individual level. Current approaches, however, do not propagate the uncertainty in observations due to sampling variability in the groups. This effectively assumes that small and large groups are estimated equally well, and should have equal weight in the final regression. We construct a Bayesian distribution regression formalism that accounts for this uncertainty, improving the robustness and performance of the model when group sizes vary. We frame the model in a neural network style, allowing for simple MAP inference using backpropagation to learn the parameters, as well as MCMC-based inference which can fully propagate uncertainty. We demonstrate our approach on illustrative toy datasets, as well as on an astrostatistics problem in which velocity distributions are used to predict galaxy cluster masses, quantifying the distribution of dark matter in the universe." @default.
- W2613950051 created "2017-05-19" @default.
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- W2613950051 date "2017-05-11" @default.
- W2613950051 modified "2023-09-27" @default.
- W2613950051 title "Bayesian Distribution Regression." @default.
- W2613950051 hasPublicationYear "2017" @default.
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