Experiment evaluates hospital case cost prediction models using Azure ML.
problem Accurate hospital case cost modelling for efficient financial management.
method Azure Machine Learning Studio tool for comparing 14 regression models.
result Robust regression, boosted decision tree, and decision forest models outperformed others.
Locally adaptive interpretable regression improves linear regression's predictability.
problem Linear regression's predictability is limited; it lacks adaptability.
method Locally adaptive interpretable regression (LoAIR) uses neural networks to predict percentile of a Gaussian distribution for regression coefficients.
result LoAIR achieves comparable or better predictive performance than state-of-the-art baselines.
Proposes a new selective regression method using conformal prediction.
problem The need for models to abstain from predictions in cases of uncertainty.
method Leverages conformal prediction to provide grounded confidence measures for individual predictions based on model-specific biases.
result Demonstrates an advantage over state-of-the-art baselines in selective regression.
NPMR uses nuclear norm penalty for multinomial regression, predicting baseball outcomes.
problem Predicting at bat outcomes in baseball with improved accuracy.
method Nuclear penalized multinomial regression (NPMR) applied to MLB data.
result NPMR provides better prediction probabilities for batter-pitcher matchups.
The paper proposes a method for valid multi-target regression predictions.
problem Valid multi-variate predictions for multi-target regression.
method Copula functions applied to deep neural networks for inductive conformal prediction.
result The proposed method ensures efficiency and validity for multi-target regression problems.
Tomal et al. propose Regression Phalanxes for better regression predictions.
problem Improving regression prediction accuracy in high-dimensional data.
method Hierarchical clustering to automatically choose Regression Phalanxes, building models for each phalanx, ensembling.
result Regression Phalanxes improve prediction accuracy compared to effective methods like Lasso or Random Forests.
Twin neural network regression predicts differences between two data points.
problem Traditional regression methods are inaccurate for certain data sets.
method TNN regression predicts differences between two data points and averages predictions from an ensemble of all training data points.
result TNN regression yields more accurate predictions compared to other methods.
Bayesian quantile regression trees improve predictive performance.
problem Quantile regression trees for conditional quantiles are underutilized.
method Bayesian quantile additive regression trees model.
result Shows very good predictive performance in simulations and real data.
Two novel approaches improve prediction with Clusterwise Linear Regression.
problem Predicting cluster labels for unseen test points in CLR.
method Two novel approaches: Predictive CLR and Constrained CLR.
result Both approaches significantly improve over known CLR-based regression methods.
New method creates adaptive prediction intervals for regression models.
problem Need to quantify uncertainty in regression model predictions.
method Regression trees and Random Forests trained on conformity scores.
result Superior scalability and performance compared to baselines.
New method uses quantile regression for adaptive prediction intervals.
problem Constructing valid prediction intervals without distributional assumptions.
method Combines conformal prediction with classical quantile regression.
result Shows shorter, more efficient prediction intervals.
Sharp rates for prediction error in high-dimensional sparse models.
problem High-dimensional sparse linear models with limited predictive power.
method Forward regression for model selection and least squares estimation.
result Sharp convergence rates without beta-min or irrepresentability conditions.
Improved conformalized quantile regression for adaptive prediction intervals.
problem Lack of adaptiveness in the conformal step of conformalized quantile regression.
method Cluster explanatory variables by permutation importance and apply k conformal steps.
result Improved prediction intervals are more adaptive to heteroscedasticity.
Recursive prediction of graph signals with new nodes added.
problem Predicting graph signals with new nodes added over time.
method Recursive prediction of graph signals using incoming nodes.
result Recursive method results in good prediction performance close to full graph knowledge.
Enhances random forests with regression for better predictions.
problem Random forests may suffer in certain prediction problems.
method Regression-enhanced random forests (RERFs) using penalized parametric regression.
result RERFs outperform RFs in important practical situations.
Data interpolation can achieve optimal rates in nonparametric regression and prediction.
problem Achieving optimal rates in nonparametric regression and prediction.
method Interpolating the training data to achieve optimal rates.
result Interpolating the training data can achieve optimal rates for nonparametric regression and prediction.
New method calibrates uncertainty predictions for regression tasks.
problem Calibrating uncertainty in regression tasks for safety-critical applications.
method Proposes a new definition of calibration and a histogram-based evaluation method.
result Demonstrates improved calibration compared to existing methods.
The paper uses machine learning to predict volatility from option data.
problem Improving predictability and liquidity of VIX-styled volatility indices.
method Regularized regression and Feedforward Neural Networks (FNN) were tested on S&P 500 Index and its option data.
result Ridge regression and FNN improve volatility indexing with higher prediction performance and fewer options required.
New adaptive models improve prediction accuracy with missing data.
problem Improving prediction accuracy with missing data entries.
method Adaptive optimization approach, learning imputation and regression simultaneously.
result 2-10% improvement in out-of-sample accuracy in strongly non-random missing data settings.
The paper improves prediction intervals for non-parametric regression using histograms.
problem Computing accurate prediction intervals for non-parametric regression models.
method Uses conditional histograms to estimate conditional distributions and compute shortest prediction intervals.
result The method provides prediction intervals with provable marginal coverage and asymptotic conditional coverage.
Investigates methods to regularize quantile regression for accurate predictions.
problem Improving accuracy and fairness in quantile regression predictions.
method Various regularization techniques including expected pinball loss, monotonicity constraints, and rate constraints.
result Deep lattice networks can maintain non-crossing quantiles and improve calibration and fairness.
DDR method improves regression performance by predicting arbitrary quantiles.
problem Traditional regression methods produce biased mean predictions and lack robustness.
method Deep Distribution Regression (DDR) method that estimates arbitrary quantiles.
result DDR method outperforms traditional methods in mean and quantile prediction.
Enhances kernel regression with network data for better predictions.
problem Improving predictive power in high-dimensional data.
method Combines kernel regression with network cohesion data to model nonlinearities.
result Significantly better predictive performances in high-dimensional data.
A novel algorithm predicts customized allergy seasons using multi-variate triple-regression.
problem Predicting customized allergy seasons for individual patients.
method Triple-regression algorithm with pre-processing and three-stage regressions.
result Improved forecasting accuracy and reduced uncertainty.
This study improves stock price prediction for Apple Inc. using feature selection and regression models with technical indicators.
problem Improving stock price prediction accuracy for Apple Inc. using technical indicators.
method Evaluation of 123 technical indicators and 10 regression models on 13 years of Apple Inc. data.
result Combining feature selection with regression models significantly improves prediction accuracy.
We analyzed optimism in linear and kernel regression models.
problem Understanding predictive complexity in regression models.
method Derived closed-form asymptotic optimism for linear and kernel regression models.
result Scaled optimism is a useful measure for model complexity.
Proposes ridge regression on Riemannian manifolds for time-series prediction.
problem Time-series prediction on Riemannian manifolds.
method Combines Riemannian least-squares fitting via Bézier curves, empirical covariance on manifolds, and Mahalanobis distance regularization.
result Significant error reduction in synthetic spherical experiments and hurricane forecasting.
Proposes a method to estimate drug sensitivity uncertainty using deep regression forests.
problem Lack of confidence intervals in deep learning models for critical tasks.
method Uses Deep Regression Forests to estimate variance and uncertainty for drug sensitivity prediction.
result Improves efficiency and coverage of uncertainty estimates for drug sensitivity predictions.
CIR method constructs efficient prediction intervals with guaranteed coverage.
problem Efficiently constructing near-minimal prediction intervals with guaranteed coverage.
method Conditional Interquantile Regression (CIR) and CIR+ (enhanced version).
result Optimal balance between predictive accuracy and computational efficiency.
Combines prediction intervals from multiple non-disclosed sources.
problem Creating valid prediction intervals from multiple non-disclosed data sources.
method Train a conformal predictor on each data source independently and combine intervals.
result Produces valid prediction intervals with improved efficiency.
WOCR combines orthogonal components with weighted regression for improved predictive performance.
problem Improving predictive performance in multiple linear regression.
method WOCR uses orthogonal components and weights based on correlations with the response.
result Enhanced predictive performance through weighted orthogonal components.
Develops conformalized prediction intervals for bounded continuous outcomes.
problem Predicting continuous outcomes within bounded ranges, especially when models are misspecified.
method Conformal prediction intervals based on transformation regression models, accounting for heteroscedasticity and asymmetry.
result Valid finite-sample coverage confirmed in simulations and real data applications.
The paper proposes a method to produce well-calibrated predictions in regression tasks using maximum mean discrepancy.
problem The need for accurate uncertainty quantification in machine learning predictions.
method The method uses maximum mean discrepancy to minimize the kernel embedding measure and calibrate predictions.
result The method produces well-calibrated and sharp prediction intervals, outperforming state-of-the-art methods.
Collider regression improves predictive performance in regression tasks.
problem Discarding prior causal knowledge in regression tasks.
method Collider regression framework incorporating probabilistic causal knowledge from collider structures.
result Proves positive generalization benefit and provides closed-form estimators.
VSPS creates flexible prediction regions for multi-target regression with guaranteed coverage.
problem Uncertainty quantification in multi-target regression with complex distributions.
method Conditional normalizing flows with conformal calibration to identify dense regions.
result VSPS produces smaller, more informative prediction regions with robust coverage guarantees.
Approach scales regression for large datasets using CART segmentation.
problem Scaling regression to large datasets in various applications.
method Two-step approach: CART segmentation followed by model development for each segment.
result Predictive performance matches Gradient Boosted Trees.
Language models can predict numeric values as strings.
problem Regression tasks with numeric predictions.
method Causal sequence decoding models trained for next-token prediction.
result Decoder-based heads perform as well as standard heads in numeric regression tasks.
The paper improves prediction error estimation in Gaussian process regression.
problem Underestimation of prediction errors in Gaussian process regression.
method Derives a more accurate MSE bound using a generalization of the Cramér-Rao bound.
result Improves the estimation of prediction errors in Gaussian process regression.
NGBoost boosts multivariate probabilistic regression.
problem Joint probabilistic regression for multivariate targets.
method Natural Gradient Boosting for nonparametric modeling.
result Competitive performance in oceanographic velocity prediction.
The paper combines Bitcoin price models with expert corrections for better predictions.
problem Improving Bitcoin price predictions using statistical and expert insights.
method Linear regression models combined with expert corrections, utilizing Bayesian approach for fat-tailed distributions.
result Better price prediction results compared to using either model or expert opinion alone.
Improves regression model calibration to better estimate prediction uncertainty.
problem Calibration of regression model output distributions.
method Post-hoc approach using multi-output Gaussian Processes with Beta link function.
result Improves both distribution-level and quantile-level calibration.
Study evaluates ensemble methods for zero-shot uncertainty quantification with diffusion models.
problem Quantifying uncertainty in zero-shot regression problems using diffusion models.
method Used diffusion probabilistic models for ensemble prediction and evaluated their effectiveness on various regression tasks.
result Ensemble methods consistently improve model prediction accuracy across different regression tasks.
Proposes a non-crossing deep neural network quantile regression method.
problem Quantile crossing in nonparametric quantile regression.
method Non-crossing constraints via rectified linear unit penalty function.
result Established non-asymptotic upper bounds for excess risk.
New method improves probabilistic electricity price predictions.
problem Improving point forecasts to probabilistic distributions for better decision-making.
method Isotonic Distributional Regression combined with other postprocessing methods.
result Isotonic Distributional Regression outperforms other methods in combining probabilistic distributions.
Study examines how imputation accuracy affects prediction accuracy in regression problems with missing covariates.
problem Missing covariates in regression or classification problems.
method Simulation and empirical analysis using UCI datasets and statistical inference.
result Imputation accuracy impacts prediction accuracy, especially with Machine Learning methods.
Proposes GPLFR for predicting high-dimensional outputs with few data.
problem Predicting high-dimensional outputs from limited data.
method GPLFR combines Gaussian process and linear-Gaussian decoding for high-dimensional prediction.
result GPLFR outperforms existing methods in predicting high-dimensional outputs.
We consider the prediction of weak effects in a multiple-output regression setup, when covariates are expected to explain a small amount, less than ≈1, of the variance of the target variables. To facilitate the prediction of the weak effects, we constrain our model structure by introducing a novel Bayesian ap…
New bounds on efficiency for conformalized regression methods.
problem Efficiency of conformal prediction in regression models.
method Non-asymptotic bounds on prediction set length for conformalized quantile and median regression.
result Identifies phase transitions in convergence rates across different regimes of miscoverage level.