This paper examines the use of a residual bootstrap for bias correction in machine learning regression methods. Accounting for bias is an important obstacle in recent efforts to develop statistical inference for machine learning methods. We demonstrate empirically that the proposed bootstrap bias correction can lead to…
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48 results for “Bootstrap Bias Correction”
A new method improves super learner validation efficiency.
problem Improving the efficiency of super learner validation.
method Bootstrap Bias Corrected Cross Validation applied to Super Learning.
result Bootstrap Bias Corrected Cross Validation proved efficient and cost-effective.
The paper studies how to use AI-generated labels in econometrics to avoid bias.
problem Small misclassification errors in AI-generated labels can lead to large biases in econometric estimators.
method The paper proposes a coupled-label bootstrap method to correct bias and deliver valid inference.
result The coupled-label bootstrap method is valid without the strong independence condition between true and imputed labels.
We use tools from geometric statistics to analyze the usual estimation procedure of a template shape. This applies to shapes from landmarks, curves, surfaces, images etc. We demonstrate the asymptotic bias of the template shape estimation using the stratified geometry of the shape space. We give a Taylor expansion of t…
New method corrects bias in estimating entropic risk for better decision-making.
problem Underestimation of entropic risk when data are limited.
method Parametric bootstrap procedure to overestimate entropic risk.
result Corrected method provides better risk estimates, leading to improved decision-making.
Develops a simple method for creating private confidence intervals.
problem Creating private confidence intervals for parametric estimation.
method Parametric bootstrap approach to construct confidence intervals.
result The parametric bootstrap provides consistent and effective confidence intervals.
Unified framework for output analysis using Monte Carlo sampling.
problem Accurately assess the quality of estimated values in predictive models.
method Unified output analysis framework through Monte Carlo sampling, leveraging fast iterative bootstrap sampling and higher-order influence functions.
result Clear advantage in building more robust confidence intervals with higher coverage probability.
A fast bootstrap method estimates cross-validation standard error.
problem Uncertainty quantification in cross-validation estimates.
method Random-effects model to estimate variance component.
result Valid confidence intervals for model performance.
Develops a novel fast bootstrap for dependent data with higher-order accuracy.
problem Estimation of parametric and semi-parametric models for dependent data.
method i.i.d. resampling of smoothed moment indicators, asymptotic refinements under mild assumptions.
result Higher-order correct asymptotic confidence distributions and confidence intervals.
Enhanced TSFMs improve time series forecasting accuracy and reliability.
problem Variance, bias, and uncertainty in TSFMs' predictions on real data.
method Statistical and ensemble techniques including bagging, stacking, residual modeling, and prediction intervals.
result Hybrid models consistently outperform standalone TSFMs across multiple horizons.
New method corrects motion artifacts in MR images without paired data.
problem Lack of paired data for supervised training in deep learning for MR motion correction.
method Outlier-rejecting bootstrap subsampling and aggregation, using optimal transport cycleGAN.
result Outperforms existing deep learning methods in correcting motion artifacts from TSM.
Study predicts internet-based treatment effects for GPPPD based on dyadic coping.
problem Identifying which patients will benefit most from internet-based GPPPD treatment.
method Developed a multivariable decision tree model using recursive partitioning.
result Predicts large effects for high dyadic coping patients, small effects for low dyadic coping patients.
A new protocol evaluates small machine learning improvements conservatively.
problem Uncertainty in small gains reported in machine learning papers.
method Paired bootstrap protocol with BCa confidence intervals and sign-flip permutation tests.
result Conservative evaluation reduces over-claiming of small improvements.
The correct use of model evaluation, model selection, and algorithm selection techniques is vital in academic machine learning research as well as in many industrial settings. This article reviews different techniques that can be used for each of these three subtasks and discusses the main advantages and disadvantages …
A new method corrects bias in machine learning for trading by filtering out non-executable prices.
problem Contaminated data from price limits causes misleading performance metrics in trading models.
method Mask-first design with tradability filters, GPU optimization, and penalized losses.
result The system achieves Sharpe ratios of 1.63 on real data, significantly outperforming existing methods.
The bootstrap provides a simple and powerful means of assessing the quality of estimators. However, in settings involving large datasets, the computation of bootstrap-based quantities can be prohibitively demanding. As an alternative, we present the Bag of Little Bootstraps (BLB), a new procedure which incorporates fea…
Depth uncertainty networks don't improve with bias correction, contrary to expectations.
problem Improving performance in active learning with overparameterised models like NNs.
method Depth uncertainty networks, compared to underparameterised models, show no improvement in performance with bias correction.
result Depth uncertainty networks do not improve with bias correction, unlike underparameterised models.
Enhanced Survival Treesstat.ME
Enhanced survival trees improve computational efficiency and inference.
problem Censored failure time data and variable selection bias.
method Improved splitting procedure, intersected validation, fused regularization, and bootstrap-based bias correction.
result Valid confidence intervals for median survival times.
Bias correction improves language model training performance.
problem Stochastic update bias in preconditioned optimizers.
method Cross-fitted preconditioning and variance-corrected inversion.
result Reduces held-out pretraining loss by 0.15 nats.
EBQL reduces bias in Q-learning for improved performance.
problem Over- and under-estimation biases in Q-learning degrade performance.
method Ensemble Bootstrapping to reduce both over- and under-estimation biases.
result EBQL outperforms other Q-learning methods in Atari games.
Bootstrapped model learning and error correction for planning with uncertainty in model-based RLcs.LG
The paper tackles model misspecification in reinforcement learning through a bootstrapped neural network and error correction.
problem Model misspecification in reinforcement learning environments.
method Proposes a bootstrapped multi-headed neural network to learn model distributions and a global error correction filter.
result Demonstrates increased performance and stability in model accuracy and planning algorithm use.
We propose a new algorithm called PLUTO for building logistic regression trees to binary response data. PLUTO can capture the nonlinear and interaction patterns in messy data by recursively partitioning the sample space. It fits a simple or a multiple linear logistic regression model in each partition. PLUTO employs th…
We consider the performance of the bootstrap in high-dimensions for the setting of linear regression, where but is not close to zero. We consider ordinary least-squares as well as robust regression methods and adopt a minimalist performance requirement: can the bootstrap give us good confidence intervals fo…
The study examines methods to correct measurement error in nutritional epidemiology studies.
problem Measurement error in nutritional studies leads to biased and underconfident estimates.
method The article reviews various bias-correction models for exposure variables in nutritional epidemiology.
result Bias-correction methods are essential for accurate inference in nutritional studies.
This paper corrects climate model biases using a factor model approach.
problem Systematic biases in GCM outputs due to unobserved confounders.
method Factor model approach to learn latent confounders from historical data and apply them to enhance bias correction.
result Significant improvements in the accuracy of precipitation outputs.
BC-ACI corrects time series forecast bias, improving prediction intervals.
problem Persistent bias in time series forecasts leads to overly conservative prediction intervals.
method Augments ACI with an EWM estimate of forecast bias to correct nonconformity scores and re-center intervals.
result Reduces Winkler interval scores by 13-17% under distribution shifts, improving calibration.
Study proposes a new method to estimate bias-correction term for ATE estimation.
problem Estimating the bias-correction term for ATE estimation.
method Directly estimating the bias-correction term by minimizing Bregman divergence.
result Automatic covariate balancing property achieved through specific model choices.
Improved selection of best outputs from LLMs for better accuracy.
problem BoN fails to reliably find the correct answer with imperfect rewards.
method Majority-of-the-Bests (MoB) selects the mode of a bootstrapped output distribution of BoN.
result MoB consistently improves over BoN in 25 out of 30 setups.
Machine learning models predict brain age with systematic bias, corrected in this study.
problem Systematic bias in machine learning regression models for brain age prediction.
method General constrained optimization approach to correct bias.
result Our method effectively eliminates the bias from brain age predictions.
In distributed, or privacy-preserving learning, we are often given a set of probabilistic models estimated from different local repositories, and asked to combine them into a single model that gives efficient statistical estimation. A simple method is to linearly average the parameters of the local models, which, howev…
For an autonomous agent, executing a poor policy may be costly or even dangerous. For such agents, it is desirable to determine confidence interval lower bounds on the performance of any given policy without executing said policy. Current methods for exact high confidence off-policy evaluation that use importance sampl…
Bias correction needed after deep learning regression training.
problem Systematic error accumulation in deep learning regression models.
method Adjust bias of the machine learning model post-training.
result Bias correction efficiently solves error accumulation.
A new method corrects bias in high-dimensional ridge regression.
problem Inherent bias in ridge regression limits statistical efficiency and scalability.
method Iterative bias correction strategy for and Ridge-Screening method for .
result Valid inferences and asymptotic properties established for de-biased ridge estimators.
Fundamental Bias in Inverting Random Sampling Matrices with Application to Sub-sampled Newtonmath.NA
Corrects bias in random sampling matrices for improved ML methods.
problem Inversion bias in random sampling matrices hampers ML applications.
method Corrects inversion bias for various random sampling methods.
result Establishes local convergence rates for sub-sampled Newton methods.
Bayesian method corrects bias in imbalanced datasets.
problem Prevalence bias in machine learning datasets.
method Bayesian risk minimization framework, bias-corrected loss function.
result Corrected loss function improves model performance.
AI-generated variables bias regression estimates; methods correct for invalid inference.
problem Bias in regression estimates due to AI-generated variables.
method Two methods: bias correction and joint estimation.
result Valid inference restored through proposed methods.
A neural framework corrects bias in estimating individual treatment effects.
problem Estimating individual treatment effects from observational data.
method An anchored neural architecture and precision-corrected intersection-bound inference.
result Corrected bias and maintained nominal coverage in high-dimensional settings.
Ribbon: Scalable Approximation and Robust Uncertainty Quantification
problem Reliably quantifying predictive uncertainty for complex models
method Ribbon, a scalable approximation to Dirichlet-reweighted bootstrap uncertainty
result Asymptotically equivalent to a flat-prior Laplace approximation under correct likelihood specification, recovers robust sandwich covariance under misspecification
New algorithm speeds up causal inference for large data.
problem Efficiently estimating causal effects from large data.
method Causal Bag of Little Bootstraps (cBLOBS).
result Significantly improved computational efficiency with consistent estimates.
WeMix improves data augmentation by correcting bias in deep learning.
problem Data augmentation's effectiveness is limited by data bias.
method Developed AugDrop and MixLoss algorithms to correct data bias.
result WeMix improves data augmentation performance through bias correction.
Paper corrects bias in online learning algorithms with endogenous data.
problem Dynamic selection problems in online learning algorithms with endogenous data.
method Instrumental-variable-based algorithm to correct bias, proving central limit theorem.
result Obtains true parameter values and low regret levels.
Bayesian inference corrects bias in supervised learning datasets.
problem Correcting bias in supervised machine learning datasets.
method Bayesian inference framework to adjust posterior distribution.
result Improved model fitting to original dataset.
Iterative method 'Concent' corrects spectrum bias in covariance matrices.
problem Consistent bias in the spectrum of covariance matrices.
method 'Concent' iterative algorithm.
result Corrects spectrum bias for small and moderate dimensions.
SIREN protocol corrects optimistic winner's scores in LLM evaluation.
problem Optimistic winner's scores in LLM evaluation due to adaptive benchmarking.
method SIREN protocol that freezes post-search shortlist, separates selection and evaluation, and uses bootstrap for uncertainty quantification.
result SIREN provides valid confidence intervals for procedure-performance curves and deployment conclusions.
The paper corrects biases in estimating intrinsic dimension and differential entropy.
problem Systematic bias in estimating intrinsic dimension and differential entropy.
method A bias-corrected estimator for both measures is proposed, highlighting shared steps and useful consequences.
result Simultaneous estimation of differential entropy and intrinsic dimension provides complementary perspectives on underlying manifolds.
The paper corrects bias in synthetic data for imbalanced learning.
problem Challenges in balancing false positive and negative rates in imbalanced data.
method Proposes a bias correction procedure to generate synthetic data for minority groups.
result Enhances prediction accuracy while avoiding overfitting.
Resampling outperforms reweighting for correcting biased data in machine learning models.
problem Correcting sampling bias in machine learning models trained on biased data sets.
method Compared resampling and reweighting techniques, focusing on their performance with stochastic gradient algorithms.
result Resampling outperforms reweighting when combined with stochastic gradient algorithms.
The paper corrects bias in fluid approximation for better decision-making in stochastic optimization.
problem Bias introduced by using mean values in fluid approximation leads to suboptimal decisions.
method Identifying a decision-corrected point estimate that yields optimal decisions.
result A corrected point estimate exists under certain conditions and can be computed algorithmically.