A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Optimizes a small set of centroid points to approximate bootstrap distribution.
GANs generate samples from time series data.
A novel deep bootstrap framework for nonparametric regression using conditional diffusion models.
A new algorithm improves stochastic linear bandit performance using residual bootstrap.
Higher bootstrap rates than 1.0 improve random forest performance.
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…
Thompson sampling provides a solution to bandit problems in which new observations are allocated to arms with the posterior probability that an arm is optimal. While sometimes easy to implement and asymptotically optimal, Thompson sampling can be computationally demanding in large scale bandit problems, and its perform…
FAB combines flows with AIS to approximate complex distributions.
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…
Data augmented bootstrap unifies various confidence interval construction methods.
A new method reduces bootstrap simulation cost and improves accuracy.
A new estimator combines bootstrapping and rollout methods in RL.
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.
New DP bootstrap method for statistical inference with improved privacy and accuracy.
A neural network estimates sampling distributions for hard problems where classical methods fail.
The paper provides rigorous guarantees for m-out-of-n bootstrap estimators of sample quantiles.
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…
Validates network bootstraps for uncertainty quantification in network visualisation.
Paper presents methods to create stock price confidence intervals using LSTM models.
A novel bootstrap method improves concept drift detection in predictive models.
Variational inference is a general approach for approximating complex density functions, such as those arising in latent variable models, popular in machine learning. It has been applied to approximate the maximum likelihood estimator and to carry out Bayesian inference, however, quantification of uncertainty with vari…
Neural Bootstrapper reduces bootstrapping cost for deep neural networks.
Twin-Boot integrates uncertainty estimation into optimization using parallel training of identical models.
Bootstrap method for Markov chains in reinforcement learning.
New test detects differences in heterogeneous datasets.
The paper analyzes bootstrap ensemble classifiers in high-dimensional settings.
Develops statistical confidence sets for multidimensional scaling.
The paper improves the empirical bootstrap method for non-normal estimators.
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…
Proposes a private empirical bootstrap for Gaussian Differential Privacy.
Develops a simple method for creating private confidence intervals.
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…
Sampling with replacement occurs in many settings in machine learning, notably in the bagging ensemble technique and the .632+ validation scheme. The number of unique original items in a bootstrap sample can have an important role in the behaviour of prediction models learned on it. Indeed, there are uncontrived exampl…
We present a new active sampling method we call min-margin which trains multiple learners on bootstrap samples and then chooses the examples to label based on the candidates' minimum margin amongst the bootstrapped models. This extends standard margin sampling in a way that increases its diversity in a supervised manne…
New algorithms improve spectral clustering for finite mixture models.
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…
BNEM improves Boltzmann sampler efficiency.
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 …
This technical note presents a new approach to carrying out the kind of exploration achieved by Thompson sampling, but without explicitly maintaining or sampling from posterior distributions. The approach is based on a bootstrap technique that uses a combination of observed and artificially generated data. The latter s…
We address the problem of prescribing an optimal decision in a framework where the cost function depends on uncertain problem parameters that need to be learned from data. Earlier work proposed prescriptive formulations based on supervised machine learning methods. These prescriptive methods can factor in contextual in…
Ribbon: Scalable Approximation and Robust Uncertainty Quantification
This paper presents an alternative approach to p-values in regression settings. This approach, whose origins can be traced to machine learning, is based on the leave-one-out bootstrap for prediction error. In machine learning this is called the out-of-bag (OOB) error. To obtain the OOB error for a model, one draws a bo…
Flow AIS Bootstrap improves flow training by generating samples in hard-to-reach regions.
When there is a distributional shift between data used to train a predictive algorithm and current data, performance can suffer. This is known as the domain adaptation problem. Bootstrap aggregating, or bagging, is a popular method for improving stability of predictive algorithms, while reducing variance and protecting…
We investigate the use of bootstrapping in the bandit setting. We first show that the commonly used non-parametric bootstrapping (NPB) procedure can be provably inefficient and establish a near-linear lower bound on the regret incurred by it under the bandit model with Bernoulli rewards. We show that NPB with an approp…
Algorithm optimizes biological sequences using bootstrapped training with a score-conditioned generator.
We quantify uncertainty in Oja's algorithm's leading eigenvector estimation.