arXiv research

Optimizes a small set of centroid points to approximate bootstrap distribution.

AR-Sieve Bootstrap improves Random Forest time series prediction accuracy.

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…

New bootstraps improve speed and accuracy for graph count functionals.

A new method reduces bootstrap simulation cost and improves accuracy.

Three bootstrap tests compare categorical time series generating processes.

Paper explores using bootstrap methods to improve SGD's stability and robustness.

Bootstrap method for Markov chains in reinforcement learning.

Proposes a private empirical bootstrap for Gaussian Differential Privacy.

The bootstrap provides a simple and powerful means of assessing the quality of estimators. However, in settings involving large datasets---which are increasingly prevalent---the computation of bootstrap-based quantities can be prohibitively demanding computationally. While variants such as subsampling and the $m$ out o…

Validates network bootstraps for uncertainty quantification in network visualisation.

New DP bootstrap method for statistical inference with improved privacy and accuracy.

Neural Bootstrapper reduces bootstrapping cost for deep neural networks.

A new bootstrapping method reduces key sizes and runtime in FHE.

The bootstrap provides a simple and powerful means of assessing the quality of estimators. However, in settings involving large datasets, the computation of bootstrap-based quantities can be prohibitively demanding. As an alternative, we present the Bag of Little Bootstraps (BLB), a new procedure which incorporates fea…

A new algorithm improves stochastic linear bandit performance using residual bootstrap.

Paper improves bootstrapping for off-policy reinforcement learning inference.

Data augmented bootstrap unifies various confidence interval construction methods.

A new online bootstrap method for time series data.

Equity-Directed Bootstrapping improves model performance across groups in imbalanced datasets.

Delta method vs Bootstrap for deep learning classification shows strong linear relationship and faster computation.

Recently, multilayer bootstrap network (MBN) has demonstrated promising performance in unsupervised dimensionality reduction. It can learn compact representations in standard data sets, i.e. MNIST and RCV1. However, as a bootstrap method, the prediction complexity of MBN is high. In this paper, we propose an unsupervis…

A wild bootstrap method for nonparametric hypothesis tests based on kernel distribution embeddings is proposed. This bootstrap method is used to construct provably consistent tests that apply to random processes, for which the naive permutation-based bootstrap fails. It applies to a large group of kernel tests based on…

In this paper we address the problem of performing statistical inference for large scale data sets i.e., Big Data. The volume and dimensionality of the data may be so high that it cannot be processed or stored in a single computing node. We propose a scalable, statistically robust and computationally efficient bootstra…

Paper presents methods to create stock price confidence intervals using LSTM models.

The paper analyzes bootstrap ensemble classifiers in high-dimensional settings.

Efficient exploration in complex environments remains a major challenge for reinforcement learning. We propose bootstrapped DQN, a simple algorithm that explores in a computationally and statistically efficient manner through use of randomized value functions. Unlike dithering strategies such as epsilon-greedy explorat…

Bootstrapping regularizes singular correlation matrices, reducing the need for complex regularization.

New algorithm speeds up causal inference for large data.

The paper improves the empirical bootstrap method for non-normal estimators.

In this paper, we propose a bootstrap method applied to massive data processed distributedly in a large number of machines. This new method is computationally efficient in that we bootstrap on the master machine without over-resampling, typically required by existing methods \cite{kleiner2014scalable,sengupta2016subsam…

Regularization is an important component of predictive model building. The hybrid bootstrap is a regularization technique that functions similarly to dropout except that features are resampled from other training points rather than replaced with zeros. We show that the hybrid bootstrap offers superior performance to dr…

Structural equation models and Bayesian networks have been widely used to study causal relationships between continuous variables. Recently, a non-Gaussian method called LiNGAM was proposed to discover such causal models and has been extended in various directions. An important problem with LiNGAM is that the results a…

A novel deep bootstrap framework for nonparametric regression using conditional diffusion models.

We introduce a bootstrap procedure for high-frequency statistics of Brownian semistationary processes. More specifically, we focus on a hypothesis test on the roughness of sample paths of Brownian semistationary processes, which uses an estimator based on a ratio of realized power variations. Our new resampling method,…

It is shown that bootstrap approximations of support vector machines (SVMs) based on a general convex and smooth loss function and on a general kernel are consistent. This result is useful to approximate the unknown finite sample distribution of SVMs by the bootstrap approach.

Develops statistical confidence sets for multidimensional scaling.

LOBSTUR-GNN adapts bootstrapping for unsupervised GNNs, improving node representation learning.

Bootstrap bounds on Einstein manifolds using semidefinite programming.

Paper improves confidence set construction for SGD using multiplier bootstrap.

Off-policy reinforcement learning aims to leverage experience collected from prior policies for sample-efficient learning. However, in practice, commonly used off-policy approximate dynamic programming methods based on Q-learning and actor-critic methods are highly sensitive to the data distribution, and can make only …

The paper explores how prior functions and bootstrapping improve ensemble uncertainty estimation.

In distributed, or privacy-preserving learning, we are often given a set of probabilistic models estimated from different local repositories, and asked to combine them into a single model that gives efficient statistical estimation. A simple method is to linearly average the parameters of the local models, which, howev…

Gaussian and bootstrap methods improve ATE estimator accuracy.

Ribbon: Scalable Approximation and Robust Uncertainty Quantification

Paper develops methods for statistical inference with SGD in nonconvex optimization.

In this paper we revisit the weighted likelihood bootstrap, a method that generates samples from an approximate Bayesian posterior of a parametric model. We show that the same method can be derived, without approximation, under a Bayesian nonparametric model with the parameter of interest defined as minimising an expec…

We provide comments on the article "High-dimensional simultaneous inference with the bootstrap" by Ruben Dezeure, Peter Buhlmann and Cun-Hui Zhang.