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.
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.
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.
Differentiable PF via entropy-regularized OT for better inference.
problem Non-differentiability of traditional PF resampling methods.
method Entropy-regularized optimal transport for differentiable resampling.
result Convergent differentiable PF method with improved gradient estimates.
A method makes particle filters differentiable without altering their forward pass.
problem Compatibility issues between particle filters and automatic differentiation.
method Introduces a correction to particle weights using the stop-gradient operator.
result Automatic differentiation produces good estimators for gradients and second-order derivatives.
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.
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…
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.
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.
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.
Class imbalance problem is commonly faced while developing machine learning models for real-life issues. Due to this problem, the fitted model tends to be biased towards the majority class data, which leads to lower precision, recall, AUC, F1, G-mean score. Several researches have been done to tackle this problem, most…
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.
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.
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.
Pathfinder uses quasi-Newton optimization for variational inference.
problem Approximating complex posterior distributions efficiently.
method Pathfinder combines quasi-Newton optimization with variational methods to approximate log densities.
result Pathfinder produces draws with lower KL divergence than ADVI and comparable to HMC, requiring fewer evaluations.
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.
Online class imbalance learning constitutes a new problem and an emerging research topic that focusses on the challenges of online learning under class imbalance and concept drift. Class imbalance deals with data streams that have very skewed distributions while concept drift deals with changes in the class imbalance s…
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…
Private statistical inference methods improve confidence interval lengths.
problem Constructing private confidence intervals with differential privacy.
method Proposed two private variants of non-parametric bootstrap.
result Achieve similar coverage accuracy to non-private methods with shorter intervals.
Multi-sample, importance-weighted variational autoencoders (IWAE) give tighter bounds and more accurate uncertainty estimates than variational autoencoders (VAE) trained with a standard single-sample objective. However, IWAEs scale poorly: as the latent dimensionality grows, they require exponentially many samples to r…
The paper improves reinforcement learning stability and efficiency with a new theoretical framework.
problem Stability and efficiency in reinforcement learning, especially in data-scarce scenarios.
method Theoretical framework using resampled U- and V-statistics to model experience replay, applied to policy evaluation and kernel ridge regression. result Significant improvements in stability and efficiency, particularly in data-scarce scenarios.
A method for optimal Bayesian filtering using progressive particle flow and optimal transport maps.
problem Optimizing Bayesian filtering with deterministic particles to avoid degeneration.
method Progressive flow of particles through a sequence of sub-steps, each using an optimal transport map to replace non-equally weighted particles with equally weighted ones.
result The method avoids particle degeneration and simplifies the filtering process by not requiring inversions or monotonicity constraints.
Importance sampling (IS) is a common reweighting strategy for off-policy prediction in reinforcement learning. While it is consistent and unbiased, it can result in high variance updates to the weights for the value function. In this work, we explore a resampling strategy as an alternative to reweighting. We propose Im…
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.
Robustly computes intrinsic coordinates on point clouds using resampling and averaging.
problem Computing intrinsic coordinates on noisy or outlier-prone point clouds.
method Subsample data, vary hyperparameters, cluster candidate embeddings, identify representative embeddings, and average them using Procrustes analysis.
result Robust to noise and outliers, validated on synthetic and real data.
CARVE validates clustering results using resampling and stability analysis.
problem Inconsistent and unreliable clustering results due to algorithm, preprocessing, and k sensitivity. method CARVE uses resampling-based validation and stability analysis to evaluate multiple clustering algorithms and hyperparameters.
result CARVE consistently recovers near-optimal clusterings and finer biological structure.
The infinitesimal jackknife (IJ) has recently been applied to the random forest to estimate its prediction variance. These theorems were verified under a traditional random forest framework which uses classification and regression trees (CART) and bootstrap resampling. However, random forests using conditional inferenc…
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.
Proposes a resampling method to compare uplift models with uncertainty.
problem Uncertainty in estimating uplift curves when full population data is unavailable.
method Two-step sampling procedure and resampling-based approach.
result Validates the proposed method through simulations and real data applications.
OTSL improves structure learning accuracy with out-of-sample and resampling strategies.
problem Determining optimal hyperparameters for structure learning algorithms.
method Out-of-sample Tuning for Structure Learning (OTSL) using resampling strategies.
result Improves graphical accuracy of structure learning algorithms.
A number of classification problems need to deal with data imbalance between classes. Often it is desired to have a high recall on the minority class while maintaining a high precision on the majority class. In this paper, we review a number of resampling techniques proposed in literature to handle unbalanced datasets …