quantile regression

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Most implemented papers archive 2025-07-28

30 shown of 102 papers with code (420 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.

• Implicit Quantile Networks for Distributional Reinforcement Learning
  14 Jun 2018 19 repositories listed Syntology ran 0 of 2 samples · 2 unverified
  In this work, we build on recent advances in distributional reinforcement learning to give a generally applicable, flexible, and state-of-the-art distributional variant of DQN.
• Distributional Reinforcement Learning with Quantile Regression
  27 Oct 2017 17 repositories listed Syntology ran 2 of 2 samples · 0 unverified · 1 pointer-only (licence)
  In this paper, we build on recent work advocating a distributional approach to reinforcement learning in which the distribution over returns is modeled explicitly instead of only estimating the mean.
• Conformalized Quantile Regression
  8 May 2019 5 repositories listed
  Conformal prediction is a technique for constructing prediction intervals that attain valid coverage in finite samples, without making distributional assumptions.
• A Multi-Horizon Quantile Recurrent Forecaster
  29 Nov 2017 5 repositories listed Syntology ran 4 of 5 samples · 1 unverified · 5 pointer-only (licence)
  We propose a framework for general probabilistic multi-step time series regression.
• Generalized Random Forests
  5 Oct 2016 5 repositories listed
  We propose generalized random forests, a method for non-parametric statistical estimation based on random forests (Breiman, 2001) that can be used to fit any quantity of interest identified as the solution to a set of…
• Offline RL with No OOD Actions: In-Sample Learning via Implicit Value Regularization
  28 Mar 2023 4 repositories listed Syntology ran 1 of 3 samples · 2 unverified
  This gives a deeper understanding of why the in-sample learning paradigm works, i.
• Fair Regression: Quantitative Definitions and Reduction-based Algorithms
  30 May 2019 4 repositories listed Syntology ran 2 of 2 samples · 0 unverified
  Our schemes only require access to standard risk minimization algorithms (such as standard classification or least-squares regression) while providing theoretical guarantees on the optimality and fairness of the…
• Causal Fair Machine Learning via Rank-Preserving Interventional Distributions
  24 Jul 2023 2 repositories listed
  A decision can be defined as fair if equal individuals are treated equally and unequals unequally.
• Neural Networks for Extreme Quantile Regression with an Application to Forecasting of Flood Risk
  16 Aug 2022 2 repositories listed
  We propose the EQRN model that combines tools from neural networks and extreme value theory into a method capable of extrapolation in the presence of complex predictor dependence.
• Machine learning methods for postprocessing ensemble forecasts of wind gusts: A systematic comparison
  17 Jun 2021 2 repositories listed
  Postprocessing ensemble weather predictions to correct systematic errors has become a standard practice in research and operations.
• Uncertainty Quantification in SVM prediction
  21 May 2025 1 repository listed
  Furthermore, we compare both, the existing and proposed SVM-based PI estimation models, with modern deep learning models for probabilistic forecasting tasks on benchmark datasets.
• Extended Fiducial Inference for Individual Treatment Effects via Deep Neural Networks
  4 May 2025 1 repository listed
  Furthermore, this work provides a rigorous framework for quantifying the uncertainty of deep neural networks under the neural scaling law, representing a substantial contribution to the statistical understanding of…
• Integrating Physics and Data-Driven Approaches: An Explainable and Uncertainty-Aware Hybrid Model for Wind Turbine Power Prediction
  11 Feb 2025 1 repository listed
  In this study, we propose a hybrid semi-parametric model that combines the strengths of both approaches, applied to a dataset from a wind farm with four turbines.
• Epistemic Uncertainty in Conformal Scores: A Unified Approach
  10 Feb 2025 1 repository listed
  Designed for compatibility with any Bayesian model, but equipped with distribution-free guarantees, EPICSCORE provides a general-purpose framework for uncertainty quantification in prediction problems.
• One Model to Forecast Them All and in Entity Distributions Bind Them
  26 Jan 2025 1 repository listed
  Probabilistic forecasting in power systems often involves multi-entity datasets like households, feeders, and wind turbines, where generating reliable entity-specific forecasts presents significant challenges.
• Distributed Conformal Prediction via Message Passing
  24 Jan 2025 1 repository listed
  Post-hoc calibration of pre-trained models is critical for ensuring reliable inference, especially in safety-critical domains such as healthcare.
• Quantile deep learning models for multi-step ahead time series prediction
  24 Nov 2024 1 repository listed
  Uncertainty quantification is crucial in time series prediction, and quantile regression offers a valuable mechanism for uncertainty quantification which is useful for extreme value forecasting.
• Conformalized High-Density Quantile Regression via Dynamic Prototypes-based Probability Density Estimation
  2 Nov 2024 1 repository listed
  Recent methods in quantile regression have adopted a classification perspective to handle challenges posed by heteroscedastic, multimodal, or skewed data by quantizing outputs into fixed bins.
• Generalized Distribution Prediction for Asset Returns
  15 Oct 2024 1 repository listed
  We present a novel approach for predicting the distribution of asset returns using a quantile-based method with Long Short-Term Memory (LSTM) networks.
• Quantile Regression for Distributional Reward Models in RLHF
  16 Sep 2024 1 repository listed
  This distributional approach can better capture the diversity of human values, addresses label noise, and accommodates conflicting preferences by modeling them as distinct modes in the distribution.
• EX-DRL: Hedging Against Heavy Losses with EXtreme Distributional Reinforcement Learning
  22 Aug 2024 1 repository listed
  However, these risk measures depend on the accurate estimation of extreme quantiles in the loss distribution's tail, which can be imprecise in QR-based DRL due to the rarity and extremity of tail data, as highlighted in…
• Probabilistic energy forecasting through quantile regression in reproducing kernel Hilbert spaces
  8 Aug 2024 1 repository listed
  Accurate energy demand forecasting is crucial for sustainable and resilient energy development.
• Conformal Thresholded Intervals for Efficient Regression
  19 Jul 2024 1 repository listed
  Unlike existing methods that rely on nested conformal frameworks and full conditional distribution estimation, CTI estimates the conditional probability density for a new response to fall into each interquantile…
• Distributional Regression U-Nets for the Postprocessing of Precipitation Ensemble Forecasts
  2 Jul 2024 1 repository listed
  Accurate precipitation forecasts have a high socio-economic value due to their role in decision-making in various fields such as transport networks and farming.
• Enhancing reliability in prediction intervals using point forecasters: Heteroscedastic Quantile Regression and Width-Adaptive Conformal Inference
  21 Jun 2024 1 repository listed
  Constructing prediction intervals for time series forecasting is challenging, particularly when practitioners rely solely on point forecasts.
• Conformal Load Prediction with Transductive Graph Autoencoders
  12 Jun 2024 1 repository listed
  Predicting edge weights on graphs has various applications, from transportation systems to social networks.
• Relaxed Quantile Regression: Prediction Intervals for Asymmetric Noise
  5 Jun 2024 1 repository listed Syntology ran 6 of 7 samples · 1 unverified
  Constructing valid prediction intervals rather than point estimates is a well-established approach for uncertainty quantification in the regression setting.
• Composite Quantile Regression With XGBoost Using the Novel Arctan Pinball Loss
  4 Jun 2024 1 repository listed
  In this paper, we present a smooth approximation of the pinball loss, the arctan pinball loss, that is tailored to the needs of XGBoost.
• AlignIQL: Policy Alignment in Implicit Q-Learning through Constrained Optimization
  28 May 2024 1 repository listed
  IDQL reinterprets IQL as an actor-critic method and gets weights of implicit policy, however, this weight only holds for the optimal value function.
• SEMF: Supervised Expectation-Maximization Framework for Predicting Intervals
  28 May 2024 1 repository listed
  This work introduces the Supervised Expectation-Maximization Framework (SEMF), a versatile and model-agnostic approach for generating prediction intervals with any ML model.

Syntology lines on 6 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.