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quantile regression

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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/hamrel-cxu/spci-code

공식 코드

Sequential Predictive Conformal Inference for Time Series

https://github.com/izzak98/dist-pred

공식 코드

Generalized Distribution Prediction for Asset Returns

https://github.com/jbrich95/pinnev

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Regression modelling of spatiotemporal extreme U.S. wildfires via partially-interpretable neural networks

https://github.com/kabolat/behavio-temporal-vae

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One Model to Forecast Them All and in Entity Distributions Bind Them

https://github.com/korobilis/hierarchicalbayes

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Bayesian Approaches to Shrinkage and Sparse Estimation

https://github.com/lei-ding07/conformal_quantile_fairness

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Conformalized Fairness via Quantile Regression

https://github.com/ltpritamanand/-PI-IN-SVM-

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Uncertainty Quantification in SVM prediction

https://github.com/luca-pernigo/kernel_quantile_regression

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Probabilistic energy forecasting through quantile regression in reproducing kernel Hilbert spaces

https://github.com/luo-lorry/conformal-load-forecasting

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Conformal Load Prediction with Transductive Graph Autoencoders

https://github.com/luo-lorry/cti

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Conformal Thresholded Intervals for Efficient Regression

https://github.com/marco-peg/mvqr

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Vector Quantile Regression on Manifolds

https://github.com/mpfarrho/tvp-qr

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Modeling tail risks of inflation using unobserved component quantile regressions

https://github.com/msesia/chr

공식 코드

Conformal Prediction using Conditional Histograms

https://github.com/msesia/cqr-comparison

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A comparison of some conformal quantile regression methods

https://github.com/mzaffran/conformalpredictionmissingvalues

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Conformal Prediction with Missing Values

https://github.com/nicolinho/qrm

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Quantile Regression for Distributional Reward Models in RLHF

https://github.com/nppackages/binsreg

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Binscatter Regressions

https://github.com/opasche/EQRN_Results

공식 코드

Neural Networks for Extreme Quantile Regression with an Application to Forecasting of Flood Risk

https://github.com/opasche/eqrn

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Neural Networks for Extreme Quantile Regression with an Application to Forecasting of Flood Risk

https://github.com/paidamoyo/structured_latent_odes

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Capturing Actionable Dynamics with Structured Latent Ordinary Differential Equations

https://github.com/peiman-m/ACQNP

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Adaptive Conditional Quantile Neural Processes

https://github.com/pic-romain/unet-pp

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Distributional Regression U-Nets for the Postprocessing of Precipitation Ensemble Forecasts

https://github.com/pmalekzadeh/ex-drl

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EX-DRL: Hedging Against Heavy Losses with EXtreme Distributional Reinforcement Learning

https://github.com/progbelarus/batchmultivalidconformal

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Batch Multivalid Conformal Prediction

https://github.com/quilograma/aenbmimocqr

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A general framework for multi-step ahead adaptive conformal heteroscedastic time series forecasting

https://github.com/robinzixuan/IQN_Agent

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IGN : Implicit Generative Networks

https://github.com/rotmanfinhub/gamma-vega-rl-hedging

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Gamma and Vega Hedging Using Deep Distributional Reinforcement Learning

https://github.com/rrross/uacqr

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Integrating Uncertainty Awareness into Conformalized Quantile Regression

https://github.com/ryanxhr/ivr

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Offline RL with No OOD Actions: In-Sample Learning via Implicit Value Regularization

https://github.com/salimamoukou/acpi

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Adaptive Conformal Prediction by Reweighting Nonconformity Score

https://github.com/sehwankimstat/doublenn

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Extended Fiducial Inference for Individual Treatment Effects via Deep Neural Networks

https://github.com/shai128/mqr

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Calibrated Multiple-Output Quantile Regression with Representation Learning

https://github.com/slds-lmu/paper_2023_cfml

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Causal Fair Machine Learning via Rank-Preserving Interventional Distributions

https://github.com/swager/grf

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Generalized Random Forests

https://github.com/swamiviv/CLASP

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Semantic uncertainty intervals for disentangled latent spaces

https://github.com/sydney-machine-learning/quantiledeeplearning

공식 코드

Quantile deep learning models for multi-step ahead time series prediction

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.