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.
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.
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…
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.
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.
Corrects bias in learned generative models using likelihood-free importance weighting.
problem Bias in learned generative models relative to true data distribution.
method Estimate likelihood ratio using a classifier, apply importance weighting.
result Consistently improves goodness-of-fit metrics for deep generative models.
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 p<n and Ridge-Screening method for p>n. result Valid inferences and asymptotic properties established for de-biased ridge estimators.
This work examines bias-correction methods for distributed stochastic optimization.
problem Performance of bias-correction methods in stochastic and adaptive settings.
method Exact diffusion method is studied under stochastic and adaptive settings.
result Exact diffusion outperforms traditional methods in sparsely-connected network topologies.
A deep learning model corrects precipitation bias without expert knowledge.
problem Precipitation bias in numerical predictions due to limited observation and models.
method Data-driven deep learning model with Denoising Autoencoder and Ordinal Regression blocks.
result The model achieves the best correcting performance and TS compared to classical methods.
Corrects bias in feature importance measures of tree-based methods.
problem Bias in feature importance measures of tree-based methods.
method Corrects bias by incorporating out-of-sample split-improvement.
result Better summaries and screening tools of feature importance.
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.
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…
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.
Corrects bias in Cox models using AI-extracted covariates.
problem Bias in Cox regression models due to AI-extracted covariates.
method Unified multivariate calibration framework to decompose and correct bias.
result Corrected Cox estimator reduces bias and achieves near-nominal coverage.
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.
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.
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.
Corrects bias in machine learning models by re-weighting data points.
problem Machine learning models inherit biases from training data.
method Formulates bias mathematically, corrects it by re-weighting data points.
result Training on re-weighted data yields an unbiased classifier.
New methods correct bias in LLM-as-a-Judge evaluations, but reliability depends on judge quality and model calibration.
problem Systematic bias in LLM-as-a-Judge evaluations using naive estimators.
method Analytical results, simulations, and real-data case study to diagnose reliability of corrected estimates.
result Corrected estimates, especially shared-calibration comparisons, can be unreliable under certain conditions.
The H-score can bias bicluster comparisons, but a correction is provided.
problem Bias in H-score for comparing biclusters.
method Analytical proof and simulation to demonstrate bias; correction method provided.
result The H-score can be biased towards small clusters, but a correction is possible.
New estimator corrects bias in CKA for sparsely sampled neurons.
problem Bias in CKA for sparsely sampled neurons.
method Novel estimator that corrects for input and feature sampling.
result Reliable model-to-brain alignment with sparsely sampled neurons.
Bayesian method corrects bias in class-aggregated values.
problem Bias in aggregated values from classification algorithms.
method Bayesian inference with constraints on model parameters.
result Outperforms existing methods in mean squared error.
New method improves sampling from score-based models by correcting bias.
problem Bias in sampling from score-based diffusion models.
method Metropolis-Hastings or Barker's accept-reject steps to correct bias, using the score function.
result Improves sample quality on synthetic and image datasets, yielding consistent gains in FID.
New algorithm corrects bias in LDP-released data for better analysis.
problem Bias in data released under Local Differential Privacy (LDP).
method Inverse Weierstrass Private Stochastic Gradient Descent (IWP-SGD).
result Converges to true population risk minimizer at O(1/n) rate. Active learning introduces bias; this paper fixes it.
problem Bias in active learning due to non-representative training data.
method Formalized bias, identified situations where it's harmful/helpful, introduced corrective weights.
result Corrective weights can improve active learning, especially with overparameterized models.
Few-shot image classification is improved by correcting CNNs' texture bias.
problem Few-shot image classification performance is hindered by CNNs' texture bias.
method Corrected CNNs' texture bias using a simpler method than state-of-the-art approaches.
result State-of-the-art performance on miniImageNet task achieved.
We correct for sampling bias in training models to improve real-world performance.
problem Sampling bias causes discrepancies between lab and real-world model performance.
method Bayesian risk minimization and derived bias-corrected loss functions.
result Our approach integrates seamlessly into current learning paradigms and improves model performance.
A new watermarking method corrects bias in language models using maximal coupling.
problem Correcting bias in language model token distributions.
method Maximal coupling to balance bias correction and text quality.
result Outperforms prior techniques in preserving text quality and detectability.
STAS selects optimal spatio-temporal scales for bias correction in precipitation forecasts.
problem Limited prior data and fixed ST scale in existing BCoPs lead to biases in numerical weather predictions.
method End-to-end deep-learning BCoP model STAS with SFM/TFM to automatically adjust spatial and temporal scales.
result STAS outperforms 8 published BCoP methods on threat scores (TS).
A new estimator corrects bias in high-dimensional predictive regressions.
problem Bias in high-dimensional predictive regressions.
method IVX-desparsified LASSO (XDlasso) estimator.
result Corrects both shrinkage and Stambaugh bias.
New method corrects bias in CVaR estimation for extreme risks.
problem Limited data above VaR leads to poor CVaR estimation.
method Bias-corrected peaks-over-threshold (POT) estimation using GPD.
result Asymptotically unbiased CVaR estimator with lower threshold.
Random forest models systematically bias predictions; a numerical transform corrects this.
problem Systematic bias in random forest regression predictions.
method Defined a numerical transformation using training data to correct bias.
result Improved predictions across real-world and synthetic datasets.
New method preserves GCM spatial dependencies for better climate projections.
problem Systemic biases in GCM output and loss of spatial/temporal dependencies.
method SPECD approach using Vecchia approximation and semi-parametric quantile regression.
result SPECD preserves key marginal and joint distribution properties of precipitation and temperature.
New method corrects bias in event study analysis using machine learning.
problem Bias in conventional event study analysis mechanisms.
method Topological machine-learning approach with self-organizing map (SOM).
result Identifies factors of abnormal stock returns and depicts event clusters.
We model and correct bias in sequential evaluation, improving ranking accuracy.
problem Sequential evaluation bias in online, irrevocable scoring.
method Modeling the rating process, posing as statistical inference, proposing an online algorithm.
result Near-linear time, online algorithm with guarantees in ranking metrics, information theoretically optimal.
New MCMC method corrects bias without extra cost.
problem Correcting bias in MCMC algorithms without additional computational cost.
method Generalized Markov Chain Importance Sampling methods.
result Proposed methods are more efficient than Metropolis-Hastings versions.
Distributed learning is an effective way to analyze big data. In distributed regression, a typical approach is to divide the big data into multiple blocks, apply a base regression algorithm on each of them, and then simply average the output functions learnt from these blocks. Since the average process will decrease th…