This work proves convergence of adaptive resampling for random Fourier features.
problem Sampling Fourier frequencies well for high-dimensional data.
method Data adaptive resampling of Fourier frequencies, asymptotically optimal.
result Proves convergence of adaptive resampling method for regression and classification problems.
Toy model study shows resampling/reweighting can improve feature learning in imbalanced classification.
problem Improving feature learning in imbalanced classification problems.
method High-dimensional toy model with replica method, class-wise resampling/reweighting, and simplified model.
result No resampling/reweighting can sometimes give best feature learning performance.
This study evaluates data pre-processing techniques for class imbalance in biomedical data.
problem Class imbalance in biomedical datasets affects model performance.
method Resampling and feature selection techniques evaluated using SVM, C4.5, LDA, and KNN classifiers.
result Feature Selection outperforms other methods in most cases, especially with SVM.
Framework for renewable energy forecasting and feature engineering.
problem Forecasting and feature extraction for multivariate processes in renewable energy.
method Derivative-free optimization, ensemble of sequence-to-sequence networks, additive resampling, Bootstrap aggregating.
result The proposed method outperforms other machine learning techniques in long-term forecasts and feature selection.
Simulation studies show resampling methods can be reliable for causal graph confidence.
problem Determining when causal discovery results can be trusted in real-world settings.
method Evaluation of subsampling and sampling with replacement methods.
result Subsampling and sampling with replacement performed well in indicating graph feature confidence.
Framework for explaining CNN predictions using input resampling.
problem Limited model interpretability in neural networks.
method Select neurons by two metrics over perturbed input images.
result Identifies neurons that influence and generalize network output.
Hybrid bootstrap improves model performance over dropout.
problem Improving predictive model performance through regularization.
method Resamples features from other training points instead of replacing them with zeros.
result Offers superior performance compared to dropout.
e-values rank features for model performance.
problem Feature selection for parametric models.
method Data depths and resampling-based algorithm.
result e-values distinguish essential features.
Paper uses bootstrapping to estimate ensemble methods' performance.
problem Estimating operating characteristics of ensemble methods.
method Bootstrap resampling for infinite resampling without refitting.
result Alternative methods improve predictive accuracy in meta-parameter selection.
Differentiable resampling improves particle filter performance.
problem Non-differentiability of traditional resampling in particle filters.
method Introduced a neural network resampler (particle transformer) trained with a likelihood-based loss function.
result Learned resampling outperforms traditional methods on synthetic and real-world tasks.
A new differentiable resampling method for Monte Carlo simulations.
problem Improving the efficiency and differentiability of resampling in Monte Carlo simulations.
method Proposes a diffusion model surrogate for resampling, proving consistency and outperforming existing methods.
result The proposed method outperforms state-of-the-art differentiable resampling methods on various benchmarks.
Paper proposes a feature-wise change detection method for improving indoor positioning accuracy.
problem Improving the quality of reference fingerprint maps in indoor positioning systems.
method Inspired by RANSAC, the paper uses resampling of features to estimate intermediate locations and identifies candidate locations using MJI.
result The approach improves positioning accuracy by 20% and achieves 90% change detection accuracy.
FSR efficiently discovers significant patterns with few resampled datasets.
problem Mining significant patterns in transactional data, especially subgroups.
method FSR uses resampling to bound the supremum deviation of quality statistics, providing rigorous guarantees on false discoveries.
result FSR effectively discovers significant subgroups with a small number of resampled datasets.
This paper investigates how resampling affects the accuracy of imbalanced classification tasks.
problem Achieving accurate prediction of the minority class in imbalanced datasets.
method Experimentally investigates various resampling methods and compares their impact on classification accuracy.
result Highlights key points and difficulties of resampling for imbalanced classification.
A novel two-stage resampling method improves CNN training on imbalanced colorectal cancer image data.
problem Data imbalance in medical image datasets, especially in histopathological images.
method Two-stage resampling: first oversampling in image space, then undersampling in feature space.
result The proposed method enhances CNN training on imbalanced colorectal cancer image datasets.
Queue-based resampling tackles online class imbalance learning with selective resampling of past examples.
problem Online class imbalance learning under class imbalance and concept drift.
method Queue-based resampling algorithm that selectively includes past examples in the training set.
result Queue-based resampling outperforms state-of-the-art methods in terms of learning speed and quality.
fastml guards against data leakage in automated machine learning.
problem Data leakage during preprocessing before resampling inflates apparent performance.
method fastml uses guarded resampling to re-estimate preprocessing inside each resample.
result Guarded resampling reduces apparent performance compared to global preprocessing.
A new resampling strategy, Importance Resampling, improves sample efficiency and reduces variance in off-policy prediction.
problem High variance updates in importance sampling for off-policy prediction.
method Importance Resampling (IR) resamples experience from a replay buffer and applies standard on-policy updates, avoiding importance sampling ratios.
result Importance Resampling (IR) shows improved sample efficiency and lower variance updates compared to other methods.
This paper proposes neural network-based undersampling techniques to improve model performance on class-imbalanced datasets.
problem Class imbalance problem in machine learning models leads to biased predictions and lower performance metrics.
method Neural network-based undersampling techniques applied to class-imbalanced datasets.
result Neural network-based undersampling outperforms other resampling techniques in terms of AUC, F1, and G-mean scores.
New law predicts first extinction in resampling processes.
problem Intractable extinction times in resampling processes.
method Modeling multinomial updates as independent square-root diffusions.
result Closed-form law for first-extinction time with linear cost.
This paper tackles noisy multi-objective optimization with adaptive resampling using bootstrapping.
problem Challenges in optimizing noisy multi-objective problems, especially trade-offs between exploration and exploitation.
method Adaptive resampling with bootstrapping to estimate probability of dominance and improve precision.
result Demonstrates the efficiency of the resampling approach in NSGA-II algorithm under multiple noise variations.
DeepBalance uses DBN ensembles to improve minority class prediction in imbalanced datasets.
problem Class imbalance in financial fraud detection and network intrusion analysis.
method Random DBN ensembles trained with balanced bootstraps and random feature selection.
result DeepBalance outperforms baseline resampling methods in AUC and sensitivity metrics.
Develops a semi-analytic resampling method for Lasso regression.
problem Statistical fluctuations and high computational cost in Lasso resampling.
method Semi-analytic message passing algorithm based on state evolution analysis.
result Significant reduction in computational time and improved inference accuracy.
Generates diverse images by resampling specific parts while maintaining global consistency.
problem Creating diverse images while maintaining global consistency in certain parts.
method Developed a new network architecture, training procedure, and resampling algorithm.
result Achieved low distortion block-resampling with spatially stochastic networks.
This review explores resampling techniques for imbalanced binary classification.
problem Imbalanced classes lead to poor prediction results in classification.
method Classical, cost-sensitive, and Neyman-Pearson paradigms with resampling techniques and classification methods.
result Complex dynamics among resampling techniques, base methods, metrics, and imbalance ratios.
DAIS improves AIS by resampling, avoiding gradient issues.
problem Low effective sample size in DAIS.
method DAIS with resampling step to improve efficiency.
result Resampling step avoids gradient variance issues.
Study shows resampling can drastically alter PCA results.
problem Stability and sensitivity of PCA under data resampling.
method Analyzed resampling sensitivity of high-dimensional PCA.
result PCA's principal components become asymptotically orthogonal when resampling is significant.
Study compares resampling methods for rare event prediction in longitudinal studies.
problem Predicting rare events in longitudinal follow-up studies.
method Comparison of resampling methods to improve standard regression models.
result Effect of sampling rate on model predictive performance.
A framework infers feature importance with uncertainties for high-dimensional data.
problem Estimating feature importance in high-dimensional data with uncertainty.
method Shapley value based framework, sub-SAGE, bootstrapping.
result Uncertainties in feature importance can be estimated from bootstrapping.
A new algorithm resamples Bernoulli race particle filters using true weights.
problem Handling intractable weights in particle filters.
method Proposes a novel resampling method using true weights with an unbiased estimator.
result Demonstrates lower variance in filtering estimates compared to standard methods.
We revisit the problem of feature selection in linear discriminant analysis (LDA), that is, when features are correlated. First, we introduce a pooled centroids formulation of the multiclass LDA predictor function, in which the relative weights of Mahalanobis-transformed predictors are given by correlation-adjusted t…
This paper investigates bias in resampled backtests for financial portfolios, finding it often negligible.
problem Bias in resampled backtests for financial portfolio evaluation.
method Investigation of bias in rolling-window mean-variance portfolios using resampling techniques.
result The bias in Sharpe Ratio estimates from IID resampling is often a fraction of estimation noise, making it tolerable.
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.
Improved particle filters for estimating model parameters using differentiable resampling.
problem Inability to differentiate sampling and resampling steps in particle filters.
method Extended reparameterisation trick to include stochastic input, enabling differentiation. Used p-MCMC and NUTS for parameter estimation.
result NUTS improves mixing of Markov chain and produces more accurate results in less time.
Efficient method for resampling problems using vector approximate message passing.
problem Computational demand in resampling techniques for statistical inference and ensemble learning.
method Combination of replica method from statistical physics and vector approximate message passing from information theory.
result Fast convergence and high approximation accuracy for variable selection problems.
This chapter reviews ML resampling methods for cybersecurity.
problem Estimating ML performance in cybersecurity.
method Resampling techniques for error rate and AUC estimation.
result Established a theoretical framework for ML resampling methods.
Bayesian neural networks improve reliability in multimedia forensics.
problem Challenges with out-of-distribution data in multimedia authentication.
method Proposes Bayesian neural networks (BNN) for forensic tasks.
result BNNs provide distributions for better reliability and out-of-distribution detection.
New test identifies dependency in multivariate data without resampling.
problem Identifying dependency in large multivariate datasets.
method Sequential coarse-to-fine discretization and 2x2 contingency tables.
result Strong control of family-wise error rate without resampling.
Package {mlr3spatiotempcv} simplifies spatiotemporal resampling methods in R.
problem Assessing and tuning spatial and spatiotemporal machine learning models.
method Integrates various spatiotemporal resampling methods into the {mlr3} framework.
result Provides a consistent interface for spatiotemporal resampling methods.
ART adapts class-wise resampling to improve imbalanced classification performance.
problem Class imbalance in classification tasks limits model performance.
method ART uses adaptive resampling based on class-wise performance metrics.
result ART consistently outperforms other methods on diverse benchmarks.
This paper addresses GE estimation in non-standard settings using various resampling methods.
problem Biased GE estimates in non-standard settings like clustered data and concept drift.
method Tailored resampling methods for clustered, spatial, unequal sampling, concept drift, and hierarchically structured outcomes.
result Standard resampling methods often yield biased GE estimates in non-standard settings.
A new particle filter avoids resampling to improve state estimation in high dimensions.
problem Particle deprivation in high-dimensional state spaces.
method A resampling-free particle filter designed to mitigate particle deprivation.
result The filter offers a near-accurate representation of the posterior distribution in high-dimensional contexts.
Paper uses K-NN resampling to simulate and evaluate LOB markets.
problem Simulating and evaluating limit order book (LOB) markets.
method Applies K-nearest neighbor (K-NN) resampling to LOB simulation and evaluation. result Demonstrates the effectiveness and efficiency of K-NN resampling in LOB simulation and evaluation. It is known that evolution strategies in continuous domains might not converge in the presence of noise. It is also known that, under mild assumptions, and using an increasing number of resamplings, one can mitigate the effect of additive noise and recover convergence. We show new sufficient conditions for the converge…
The outcome of a functional genomics pipeline is usually a partial list of genomic features, ranked by their relevance in modelling biological phenotype in terms of a classification or regression model. Due to resampling protocols or just within a meta-analysis comparison, instead of one list it is often the case that …
This paper enhances stability selection by evaluating overall results robustness and identifying optimal regularization values.
problem Improving the robustness and reliability of high-dimensional variable selection.
method Developed a stability estimator to evaluate stability of stability selection results, calibrating key parameters.
result Identified optimal regularization value and improved stability of variable selection.
Adversarial nets learn independent features from joint distributions.
problem Learning independent features from complex joint distributions.
method Adversarial objectives to optimize mutual information implicitly.
result Adversarial nets can solve both linear and non-linear ICA problems.
Study uses online data to identify malicious websites, addressing imbalance.
problem Identifying malicious websites from many more benign ones.
method Integrated resampling approach combining SMOTE and PSO.
result Proposed approach outperforms other resampling methods.