The network jackknife provides conservative variance estimates for network statistics.
problem Estimating the variance of network statistics.
method Leave-node-out jackknife procedure for network data under the sparse graphon model.
result The network jackknife leads to conservative estimates of the variance for network functionals invariant to node permutation.
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…
Paper proposes diagnostics for error and variance estimation in randomized matrix computations.
problem Safe use of randomized matrix algorithms in applications.
method Leave-one-out error estimator and jackknife resampling method.
result Provides rapid diagnostics to assess quality of randomized matrix computations.
Frequentist method estimates uncertainty in RNNs without altering architecture.
problem Uncertainty quantification in RNNs for decision-making.
method Jackknife resampling and influence functions to estimate variability.
result The method provides theoretical coverage guarantees on uncertainty intervals.
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.
This chapter is dedicated to the assessment and performance estimation of machine learning (ML) algorithms, a topic that is equally important to the construction of these algorithms, in particular in the context of cyberphysical security design. The literature is full of nonparametric methods to estimate a statistic fr…
We study the variability of predictions made by bagged learners and random forests, and show how to estimate standard errors for these methods. Our work builds on variance estimates for bagging proposed by Efron (1992, 2012) that are based on the jackknife and the infinitesimal jackknife (IJ). In practice, bagged predi…
Extends Infinitesimal Jackknife for model covariance, enhancing ensemble model analysis.
problem Estimating uncertainty in combinations of models.
method Extends Infinitesimal Jackknife to estimate covariance between models.
result Theoretical consistency of Infinitesimal Jackknife covariance estimate demonstrated.
Proposes a new jackknife method for time series hyperparameter selection.
problem Hyperparameter selection for time series models.
method Artificial delete-d jackknife approach.
result Asymptotic and finite-sample advantages demonstrated.
Jackknife variance estimation validated for generalized U-statistics.
problem Uncertainty quantification for subsampling-based estimators.
method Jackknife variance estimation for generalized U-statistics with row-wise Lr weak law. result Jackknife and delete-d variance estimators are ratio-consistent for generalized U-statistics. Cluster jackknife improves inference for staggered DID methods.
problem Over-rejection of CSDID in small clusters or treated clusters.
method Cluster jackknife for CSDID inference.
result Cluster jackknife greatly improves inference for CSDID.
Discriminative jackknife estimates deep learning uncertainty.
problem Quantifying uncertainty in deep learning models.
method Discriminative jackknife using influence functions of loss.
result DJ satisfies frequentist coverage and discriminative accuracy.
Paper proposes a new method to improve BART model predictions outside training data range.
problem Improving prediction and prediction intervals for BART models at extrapolation points.
method Gaussian processes are added to BART leaf nodes for extrapolation.
result The new method outperforms standard BART and frequentist resampling methods in simulations.
New algorithm uses control variates to improve multi-armed bandit performance.
problem Stochastic multi-armed bandits with auxiliary reward information.
method Developed UCB-CV algorithm using control variates for mean estimation.
result UCB-CV algorithm provides tighter confidence bounds and smaller variance.
Modified jackknife method improves predictive inference for time series data.
problem Lack of exchangeability and temporal dependence in time series data.
method Leave-a-window-out (LWO) method modification of the jackknife.
result LWO method achieves valid coverage in time series models with mild temporal dependence.
Expands statistical background for knee osteoarthritis treatment models.
problem Developing optimal exercise and weight loss treatments for knee osteoarthritis.
method Precision medicine models and jackknife cross-validation method.
result Jackknife estimator provides consistent value function estimation.
Enhances polynomial chaos models with uncertainty intervals.
problem Uncertainty quantification in surrogate models.
method Jackknife-based conformal prediction integrated into polynomial chaos expansions.
result Produces accurate predictive intervals for low-accuracy models.
We consider the performance of the bootstrap in high-dimensions for the setting of linear regression, where p<n but p/n is not close to zero. We consider ordinary least-squares as well as robust regression methods and adopt a minimalist performance requirement: can the bootstrap give us good confidence intervals fo…
Cross validation (CV) and the bootstrap are ubiquitous model-agnostic tools for assessing the error or variability of machine learning and statistical estimators. However, these methods require repeatedly re-fitting the model with different weighted versions of the original dataset, which can be prohibitively time-cons…
Adaptive method improves prediction intervals with global coverage guarantees and local error distribution.
problem Global coverage guarantees of conformal regression are often violated by local error distributions.
method Adaptive Conformal Regression with Jackknife+ Rescaled Scores
result Improves local coverage without sacrificing global coverage, especially in low-data regimes.
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.
The paper proposes a method to improve fairness in machine learning models without refitting.
problem Mitigating biases in machine learning models that disadvantage certain groups.
method Infinitesimal jackknife-based approach to drop selected training data points.
result The intervention improves fairness without significantly reducing predictive performance.
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.
JAWS audits predictive uncertainty under covariate shift using jackknife+ weighted methods.
problem Auditing predictive uncertainty under data distribution shifts.
method JAW and JAWA methods for distribution-free uncertainty quantification.
result JAW relaxes the jackknife+'s assumption of data exchangeability for covariate shift.
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.
Conformal prediction improves prediction intervals for PCEs, especially in sparse cases.
problem Quantifying local model errors in PCEs for small datasets.
method Integration of conformal prediction methods (full and Jackknife+) into full and sparse PCEs.
result Better-calibrated prediction intervals for both full and sparse PCEs.
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.
Adaptive classification methods ensure correct prediction intervals.
problem Developing methods to ensure correct prediction intervals for classification problems.
method Specialized conformal inference techniques combining cross-validation+, jackknife+, and a novel conformity score.
result The methods provide guaranteed approximate conditional coverage for complex data distributions.
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.
New methods improve prediction intervals across multiple environments.
problem Valid confidence intervals and sets in multi-environment prediction.
method Extended jackknife and split-conformal methods, with resizing for problem difficulty.
result Distribution-free coverage achieved in non-traditional data scenarios.
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.
The paper derives uniform stability-based coverage bounds for conformal prediction methods.
problem Establishing theoretical guarantees for conformal prediction methods.
method Uniform stability perspective applied to full-conformal, jackknife+, and CV+ prediction regions.
result Coverage bounds for finite-dimensional models derived using a concentration argument.
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.
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.
Paper accelerates conformal prediction by using approximate leave-one-out estimators.
problem Limited computational cost for conformal prediction.
method Incorporates approximate leave-one-out estimators to accelerate conformal prediction.
result ALO-based methods achieve comparable coverage and efficiency to exact methods but with significantly reduced runtime.
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.