quantile regression
98 papers with code • 0 benchmarks • 0 datasets
Benchmarks
These leaderboards are used to track progress in quantile regression
Most implemented papers
Implicit Quantile Networks for Distributional Reinforcement Learning
opendilab/DI-engine
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ICML 2018
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
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.
Generalized Random Forests
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 local moment equations.
A Multi-Horizon Quantile Recurrent Forecaster
We propose a framework for general probabilistic multi-step time series regression.
Conformalized Quantile Regression
Conformal prediction is a technique for constructing prediction intervals that attain valid coverage in finite samples, without making distributional assumptions.
Fair Regression: Quantitative Definitions and Reduction-based Algorithms
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 obtained solutions.
Offline RL with No OOD Actions: In-Sample Learning via Implicit Value Regularization
ryanxhr/ivr
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This gives a deeper understanding of why the in-sample learning paradigm works, i. e., it applies implicit value regularization to the policy.
Machine learning methods for postprocessing ensemble forecasts of wind gusts: A systematic comparison
Postprocessing ensemble weather predictions to correct systematic errors has become a standard practice in research and operations.
Neural Networks for Extreme Quantile Regression with an Application to Forecasting of Flood Risk
opasche/eqrn
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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.
Causal Fair Machine Learning via Rank-Preserving Interventional Distributions
A decision can be defined as fair if equal individuals are treated equally and unequals unequally.
