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.
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.
Proposes a method combining CNFs and rejection-resampling for sampling from unnormalized densities.
problem Sampling from unnormalized probability densities, especially multimodal ones.
method Combines continuous normalizing flows with rejection-resampling steps based on importance weights.
result The method improves sampling accuracy and performance compared to state-of-the-art methods.
A new method for sampling on manifolds reduces density estimation errors.
problem Sampling on implicitly defined manifolds in various applications.
method Polynomial-Maximization Moment (PMM) estimator replacing local k-nearest-neighbour density estimate.
result Reduces density estimation errors by 22--36% on asymmetric gamma and boundary-spacing regimes.
Improved Lasso estimator speeds up variable selection.
problem Efficient variable selection in high-dimensional data.
method Stability principle-based generalized debiased Lasso.
result Significantly reduces computational cost of resampling-based methods.
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.
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.
In many real-world binary classification tasks (e.g. detection of certain objects from images), an available dataset is imbalanced, i.e., it has much less representatives of a one class (a minor class), than of another. Generally, accurate prediction of the minor class is crucial but it's hard to achieve since there is…
MULTIFIT tests independence between two random vectors using multiscale Fisher's test.
problem Detecting local dependence between two random vectors.
method MULTIFIT uses a resampling-free approach to test independence.
result MULTIFIT can easily handle large sample sizes and interpret dependency nature.
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.
Proposes a new resampling method for off-policy evaluation in stochastic control.
problem Estimating policy performance from data generated under a different policy.
method K-nearest neighbor resampling procedure for off-policy evaluation.
result Statistical consistency results for the proposed method under weak conditions.
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.
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.
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.
Random Forests adapted for dependent data using GLS.
problem Random Forests' local nature fails for dependent data.
method RF-GLS: GLS loss for correlated data.
result Consistency of RF-GLS under beta-mixing processes.
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.
Active Federated Learning selects clients to maximize efficiency.
problem Minimizing bandwidth usage and maximizing model accuracy in federated learning.
method Clients are selected with a probability conditioned on the current model and client data to maximize efficiency.
result Reduces the number of required training iterations by 20-70% while maintaining the same model accuracy.
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.
Imputation method respects manifold structure for missing data.
problem Missing data imputation in high-dimensional data.
method Model-based imputation using mixture variational autoencoders and sampling-importance-resampling (SIR).
result Competitive performance and uncertainty quantification in imputations.
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.
An approximate method for conducting resampling in Lasso, the ℓ1 penalized linear regression, in a semi-analytic manner is developed, whereby the average over the resampled datasets is directly computed without repeated numerical sampling, thus enabling an inference free of the statistical fluctuations due to sam…
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…
This paper addresses the problem of filtering with a state-space model. Standard approaches for filtering assume that a probabilistic model for observations (i.e. the observation model) is given explicitly or at least parametrically. We consider a setting where this assumption is not satisfied; we assume that the knowl…
LOBSTUR-GNN adapts bootstrapping for unsupervised GNNs, improving node representation learning.
problem Hyperparameter tuning and lack of established methodologies for unsupervised GNNs.
method Adapts bootstrapping techniques for local graph dependencies and uses CCA for embedding consistency.
result 65.9% improvement in classification accuracy compared to uninformed hyperparameter selection.
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.
New STH distance finds patterns in event timeseries without resampling.
problem Lack of efficient analysis methods for event and state timeseries.
method Define STE-ts, propose STH, leveraging both time and state duration.
result Improved precision and computation time compared to resampled metrics.
Cheap methods improve uncertainty in SGD solutions.
problem Uncertainty quantification in SGD solutions.
method Two resampling-based methods: parallel resampling with replacement and online resampling.
result Significantly reduced computation effort in constructing confidence intervals.
A new online bootstrap method for time series data.
problem Applying traditional bootstrap methods to time series data with dependencies.
method An autoregressive sequence of resampling weights to account for data dependencies.
result The method provides reliable uncertainty quantification in real-time applications.
The estimation of normalizing constants is a fundamental step in probabilistic model comparison. Sequential Monte Carlo methods may be used for this task and have the advantage of being inherently parallelizable. However, the standard choice of using a fixed number of particles at each iteration is suboptimal because s…
VPR improves posterior uncertainty quantification by combining VI and predictive resampling.
problem Inaccurate posterior sampling with MCMC due to computational constraints.
method Variational predictive resampling (VPR) that uses VI's predictive strength and imputes future observations.
result VPR converges to the exact Bayesian posterior in a Gaussian location model and improves uncertainty quantification.
High-dimensional regression models struggle with resampling methods.
problem Estimating uncertainty in high-dimensional supervised regression tasks.
method Investigation of bootstrap, subsampling, and jackknife methods in high-dimensional generalized linear models.
result Resampling methods exhibit double-descent behavior and are inconsistent in high dimensions.
Framework for fair predictive models using resampled sensitive attributes.
problem Achieving fair predictions in machine learning models.
method Introducing a discrepancy functional and resampling sensitive attributes.
result Improved performance and equitable uncertainty quantification.