arXiv research

This paper tackles noisy multi-objective optimization with adaptive resampling using bootstrapping.

Differentiable resampling improves particle filter performance.

This work proves convergence of adaptive resampling for random Fourier features.

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…

Differentiable PF via entropy-regularized OT for better inference.

DAIS improves AIS by resampling, avoiding gradient issues.

Reshuffling splits improves hyperparameter optimization's generalization performance.

Paper analyzes FTPL's effectiveness in combinatorial semi-bandit problems.

Resampling outperforms reweighting for correcting biased data in machine learning models.

OTSL improves structure learning accuracy with out-of-sample and resampling strategies.

A new differentiable resampling method for Monte Carlo simulations.

The paper investigates overfitting in hyperparameter optimization.

This paper improves FTPL algorithm for semi-bandit problems with best-of-both-worlds guarantees.

Paper uses K-NN resampling to simulate and evaluate LOB markets.

ART adapts class-wise resampling to improve imbalanced classification performance.

Cheap methods improve uncertainty in SGD solutions.

Study uses stacked generalization to improve fraud detection algorithms.

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…

High-dimensional regression models struggle with resampling methods.

Many machine learning models have important structural tuning parameters that cannot be directly estimated from the data. The common tactic for setting these parameters is to use resampling methods, such as cross--validation or the bootstrap, to evaluate a candidate set of values and choose the best based on some pre--…

Proposes a resampling method to compare uplift models with uncertainty.

VPR improves posterior uncertainty quantification by combining VI and predictive resampling.

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…

CARVE validates clustering results using resampling and stability analysis.

fastml guards against data leakage in automated machine learning.

Unified framework for imbalanced data resampling improves classification performance.

New law predicts first extinction in resampling processes.

Resampling techniques are widely used in statistical inference and ensemble learning, in which estimators' statistical properties are essential. However, existing methods are computationally demanding, because repetitions of estimation/learning via numerical optimization/integral for each resampled data are required. I…

This study introduces a framework for the forecasting, reconstruction and feature engineering of multivariate processes along with its renewable energy applications. We integrate derivative-free optimization with an ensemble of sequence-to-sequence networks and design a new resampling technique called additive resampli…

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…

A novel resampling technique addresses class imbalance in imbalanced datasets.

Generates diverse images by resampling specific parts while maintaining global consistency.

We propose a new procedure for inference on optimal treatment regimes in the model-free setting, which does not require to specify an outcome regression model. Existing model-free estimators for optimal treatment regimes are usually not suitable for the purpose of inference, because they either have nonstandard asympto…

This review explores resampling techniques for imbalanced binary classification.

Efficiently bootstraps massive distributed data without over-resampling.

Study shows resampling can drastically alter PCA results.

Study compares resampling methods for rare event prediction in longitudinal studies.

Toy model study shows resampling/reweighting can improve feature learning in imbalanced classification.

The paper develops methods for constructing confidence regions for regression functions in binary classification.

A new method reduces hyperparameter tuning evaluations by using sequential tests.

A method for optimal Bayesian filtering using progressive particle flow and optimal transport maps.

RHPSVM improves SVM performance with robust loss function.

This paper investigates bias in resampled backtests for financial portfolios, finding it often negligible.

The strength of association between a pair of data vectors is represented by a nonnegative real number, called matching weight. For dimensionality reduction, we consider a linear transformation of data vectors, and define a matching error as the weighted sum of squared distances between transformed vectors with respect…

Improved particle filters for estimating model parameters using differentiable resampling.

Bayesian neural networks improve reliability in multimedia forensics.

A new UCB algorithm for heavy-tailed bandits with near-optimal regret.

An approximate method for conducting resampling in Lasso, the $\ell_1$ 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…