Paper Citation Record · LEDGER

Efficient distributional regression trees learning algorithms for calibrated non-parametric probabilistic forecasts

As of 22 August 2026, this Paper Citation Record lists 42 of 42 outbound references and 1 inbound Pith citation observation for arXiv:2502.05157.

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pith.paper-citation-record.v1
2502.05157 v3

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measured 42 of 42 reference resolution

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Pith citing papers itemized under the disclosed page cap.

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Source: cited_works

Reference resolution

42 of 42 outbound references displayed

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Outbound references

Observation 53195597-b9f5-45cf-b6f8-247e671734de · outbound

This paper cites Variational inference for nonparametric B ayesian quantile regression.

Efficient distributional regression trees learning algorithms for calibrated non-parametric probabilistic forecasts Variational inference for nonparametric B ayesian quantile regression

Reference 1

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Observation 5ce4d940-5d7c-4029-a108-386328fcc566 · outbound

This paper cites Breiman, J.

Efficient distributional regression trees learning algorithms for calibrated non-parametric probabilistic forecasts Breiman, J

Reference 2

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Observation 01ec6c74-a0a0-4de9-b6f0-14f7a698a9bf · outbound

This paper cites Brehmer and Tilmann Gneiting.

Efficient distributional regression trees learning algorithms for calibrated non-parametric probabilistic forecasts Brehmer and Tilmann Gneiting

Reference 3

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Observation f71ba2f4-156d-4e08-bca0-30be72857aef · outbound

This paper cites Towards scalable quantile regression trees.

Efficient distributional regression trees learning algorithms for calibrated non-parametric probabilistic forecasts Towards scalable quantile regression trees

Reference 4

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Observation 17ffd66a-a68d-47ef-bf32-d3fd2b02c0a8 · outbound

This paper cites Classification and regression trees.

Efficient distributional regression trees learning algorithms for calibrated non-parametric probabilistic forecasts Classification and regression trees

Reference 5

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Observation de2d7262-6b98-4228-970c-4a7f38078139 · outbound

This paper cites Ray, Tilmann Gneiting, and Nicholas G.

Efficient distributional regression trees learning algorithms for calibrated non-parametric probabilistic forecasts Ray, Tilmann Gneiting, and Nicholas G

Reference 6

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Observation ffd4b2c6-760e-408d-86a2-55aad9260cdd · outbound

This paper cites Fenchel- Y oung losses with skewed entropies for class-posterior probability estimation.

Efficient distributional regression trees learning algorithms for calibrated non-parametric probabilistic forecasts Fenchel- Y oung losses with skewed entropies for class-posterior probability estimation

Reference 7

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Observation 0d173f28-fc16-4055-95a7-f05b0deecbf6 · outbound

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Efficient distributional regression trees learning algorithms for calibrated non-parametric probabilistic forecasts Unresolved cited work

Reference 8

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This paper cites ACCRUE: A ccurate and reliable uncertainty estimate in deterministic models.

Efficient distributional regression trees learning algorithms for calibrated non-parametric probabilistic forecasts ACCRUE: A ccurate and reliable uncertainty estimate in deterministic models

Reference 9

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Observation 93c76c1c-eaf4-49c7-a8a3-b29890b94d09 · outbound

This paper cites Distributional conformal prediction.

Efficient distributional regression trees learning algorithms for calibrated non-parametric probabilistic forecasts Distributional conformal prediction

Reference 10

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Observation d61bcc5e-52d2-4f23-927d-48d2ab86a799 · outbound

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Efficient distributional regression trees learning algorithms for calibrated non-parametric probabilistic forecasts Unresolved cited work

Reference 11

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Observation bc946836-37a8-4cd4-ac55-941e3c61c646 · outbound

This paper cites Bayesian density regression.

Efficient distributional regression trees learning algorithms for calibrated non-parametric probabilistic forecasts Bayesian density regression

Reference 12

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Observation 49c74e4c-3904-4689-8396-55b74a1eefda · outbound

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Efficient distributional regression trees learning algorithms for calibrated non-parametric probabilistic forecasts Unresolved cited work

Reference 13

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Observation 6999633a-93ae-426b-a308-0602bbf7ea48 · outbound

This paper cites Gr \"u nwald and A.

Efficient distributional regression trees learning algorithms for calibrated non-parametric probabilistic forecasts Gr \"u nwald and A

Reference 14

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Observation b9beedf9-c0ae-436c-8d29-f85cf70b209c · outbound

This paper cites Nested conformal prediction and quantile out-of-bag ensemble methods.

Efficient distributional regression trees learning algorithms for calibrated non-parametric probabilistic forecasts Nested conformal prediction and quantile out-of-bag ensemble methods

Reference 15

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Observation cad26076-5dbf-4b89-8c78-42a1f7e5b6bd · outbound

This paper cites Fitting finite mixtures of generalized linear regressions in R.

Efficient distributional regression trees learning algorithms for calibrated non-parametric probabilistic forecasts Fitting finite mixtures of generalized linear regressions in R

Reference 16

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Observation cfe1a6e7-4f8e-42e4-8ee8-64f48bd300ea · outbound

This paper cites Strictly proper scoring rules, prediction, and estimation.

Efficient distributional regression trees learning algorithms for calibrated non-parametric probabilistic forecasts Strictly proper scoring rules, prediction, and estimation

Reference 17

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Observation 614dea79-afa9-4c82-95d6-47107bb2fbcc · outbound

This paper cites Halim and F.

Efficient distributional regression trees learning algorithms for calibrated non-parametric probabilistic forecasts Halim and F

Reference 18

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Observation f6f25181-7332-4a46-a30e-4896cb620ecb · outbound

This paper cites Denoising diffusion probabilistic models.

Efficient distributional regression trees learning algorithms for calibrated non-parametric probabilistic forecasts Denoising diffusion probabilistic models

Reference 19

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This paper cites Hamilton and W Viscusi.

Efficient distributional regression trees learning algorithms for calibrated non-parametric probabilistic forecasts Hamilton and W Viscusi

Reference 20

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Observation 5e7c78ba-6f84-4763-8ff5-9a199078c889 · outbound

This paper cites Evaluating probabilistic forecasts with scoringRules.

Efficient distributional regression trees learning algorithms for calibrated non-parametric probabilistic forecasts Evaluating probabilistic forecasts with scoringRules

Reference 21

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Observation ca94a7e2-3e64-476c-9cb5-5292013e78f6 · outbound

This paper cites A multiple quantile regression approach to the wind, solar, and price tracks of GEFCom2014.

Efficient distributional regression trees learning algorithms for calibrated non-parametric probabilistic forecasts A multiple quantile regression approach to the wind, solar, and price tracks of GEFCom2014

Reference 22

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Observation b4a472a2-a166-4c91-b153-f006b8abb7a8 · outbound

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Efficient distributional regression trees learning algorithms for calibrated non-parametric probabilistic forecasts Regression quantiles

Reference 23

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Observation c6c876c4-d99c-41b8-9c30-c0ba36d28e70 · outbound

This paper cites Normalizing flows: A n introduction and review of current methods.

Efficient distributional regression trees learning algorithms for calibrated non-parametric probabilistic forecasts Normalizing flows: A n introduction and review of current methods

Reference 24

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This paper cites Quantiles based personalized treatment selection for multivariate outcomes and multiple treatments.

Efficient distributional regression trees learning algorithms for calibrated non-parametric probabilistic forecasts Quantiles based personalized treatment selection for multivariate outcomes and multiple treatments

Reference 25

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Observation 7e70f6ae-9769-4389-9dff-2532f4227bfd · outbound

This paper cites Rage against the mean – a review of distributional regression approaches.

Efficient distributional regression trees learning algorithms for calibrated non-parametric probabilistic forecasts Rage against the mean – a review of distributional regression approaches

Reference 26

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Observation 0beddcaf-0d5d-4779-8eb4-5e8e66462802 · outbound

This paper cites Stepwise multiple quantile regression estimation using non-crossing constraints.

Efficient distributional regression trees learning algorithms for calibrated non-parametric probabilistic forecasts Stepwise multiple quantile regression estimation using non-crossing constraints

Reference 27

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Observation d420fea5-2a70-4364-ab47-2e3a85af2d6e · outbound

This paper cites Quantile regression forests.

Efficient distributional regression trees learning algorithms for calibrated non-parametric probabilistic forecasts Quantile regression forests

Reference 28

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Observation c446e75e-3456-46a5-8509-8c82eaa48d67 · outbound

This paper cites Handbook of data structures and applications.

Efficient distributional regression trees learning algorithms for calibrated non-parametric probabilistic forecasts Handbook of data structures and applications

Reference 29

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Observation 52eb7a2b-6bb6-435f-9a44-e044275d1939 · outbound

This paper cites Generalized maximum entropy for supervised classification.

Efficient distributional regression trees learning algorithms for calibrated non-parametric probabilistic forecasts Generalized maximum entropy for supervised classification

Reference 30

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Observation 10d7a243-f2c5-48d9-90c4-9789835f2a8b · outbound

This paper cites Nievergelt and E.

Efficient distributional regression trees learning algorithms for calibrated non-parametric probabilistic forecasts Nievergelt and E

Reference 31

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This paper cites High-resolution image synthesis with latent diffusion models.

Efficient distributional regression trees learning algorithms for calibrated non-parametric probabilistic forecasts High-resolution image synthesis with latent diffusion models

Reference 32

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Observation b178d5be-ab96-4de4-aae9-e2ca0713507a · outbound

This paper cites Handbook of quantile regression.

Efficient distributional regression trees learning algorithms for calibrated non-parametric probabilistic forecasts Handbook of quantile regression

Reference 33

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Observation d282fbdc-5b15-4a78-89da-be019df02ad9 · outbound

This paper cites Conformalized quantile regression.

Efficient distributional regression trees learning algorithms for calibrated non-parametric probabilistic forecasts Conformalized quantile regression

Reference 34

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Observation 03ddf38b-da76-4534-802a-7eb33ad40f6a · outbound

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Efficient distributional regression trees learning algorithms for calibrated non-parametric probabilistic forecasts Unresolved cited work

Reference 35

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Observation 379c63b0-acb1-4a87-8422-0dfd63f1c6c1 · outbound

This paper cites A comparison of some conformal quantile regression methods.

Efficient distributional regression trees learning algorithms for calibrated non-parametric probabilistic forecasts A comparison of some conformal quantile regression methods

Reference 36

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Observation 1a9b1b70-890f-43b1-ac1c-38b6e030327a · outbound

This paper cites Engression: Extrapolation through the lens of distributional regression.

Efficient distributional regression trees learning algorithms for calibrated non-parametric probabilistic forecasts Engression: Extrapolation through the lens of distributional regression

Reference 37

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Observation f0871fa5-a831-4309-8f3e-e2c244f55cde · outbound

This paper cites Bayesian nonparametric quantile regression using splines.

Efficient distributional regression trees learning algorithms for calibrated non-parametric probabilistic forecasts Bayesian nonparametric quantile regression using splines

Reference 38

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Observation 8a9c4aed-6c65-471e-b4a5-f007835894a7 · outbound

This paper cites Le, Timothy D.

Efficient distributional regression trees learning algorithms for calibrated non-parametric probabilistic forecasts Le, Timothy D

Reference 39

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Observation df8b7aef-49a6-4efe-8866-5c4e3d53f871 · outbound

This paper cites A review on quantile regression for stochastic computer experiments.

Efficient distributional regression trees learning algorithms for calibrated non-parametric probabilistic forecasts A review on quantile regression for stochastic computer experiments

Reference 40

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Observation 3bcd566a-0b7d-4e96-b20e-5206b07d777e · outbound

This paper cites Continuity of generalized entropy.

Efficient distributional regression trees learning algorithms for calibrated non-parametric probabilistic forecasts Continuity of generalized entropy

Reference 41

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Observation 36c18489-2944-4a92-90bd-0853ced054c5 · outbound

This paper cites Regularized simultaneous model selection in multiple quantiles regression.

Efficient distributional regression trees learning algorithms for calibrated non-parametric probabilistic forecasts Regularized simultaneous model selection in multiple quantiles regression

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T03:17:27.580404Z

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Pith citing papers

Observation 2917a9e0-c22d-41d1-afc0-e4952629bd99 · inbound

Parallel gradient boosting for flexible estimation of conditional distributions cites this paper.

Parallel gradient boosting for flexible estimation of conditional distributions Efficient distributional regression trees learning algorithms for calibrated non-parametric probabilistic forecasts

Reference 79

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