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.
This paper tackles noisy multi-objective optimization with adaptive resampling using bootstrapping.
A new online bootstrap method for time series data.
Cheap methods improve uncertainty in SGD solutions.
Efficiently bootstraps massive distributed data without over-resampling.
GANs generate samples from time series data.
Generating realistic asset-class scenarios from time series and curves
tsbootstrap handles time series uncertainty without assuming independence.
A common question being raised in automatic speech recognition (ASR) evaluations is how reliable is an observed word error rate (WER) improvement comparing two ASR systems, where statistical hypothesis testing and confidence interval (CI) can be utilized to tell whether this improvement is real or only due to random ch…
High-dimensional regression models struggle with resampling methods.
Graphical lasso models ASR utterance dependencies for consistent WER estimation.
A new method improves super learner validation efficiency.
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…
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…
Twin-Boot integrates uncertainty estimation into optimization using parallel training of identical models.
Confidence measures for the generalization error are crucial when small training samples are used to construct classifiers. A common approach is to estimate the generalization error by resampling and then assume the resampled estimator follows a known distribution to form a confidence set [Kohavi 1995, Martin 1996,Yang…
LOBSTUR-GNN adapts bootstrapping for unsupervised GNNs, improving node representation learning.
AR-Sieve Bootstrap improves Random Forest time series prediction accuracy.
Bagging is a device intended for reducing the prediction error of learning algorithms. In its simplest form, bagging draws bootstrap samples from the training sample, applies the learning algorithm to each bootstrap sample, and then averages the resulting prediction rules. We extend the definition of bagging from stati…
Neural Bootstrapper reduces bootstrapping cost for deep neural networks.
In this paper we present a technique for using the bootstrap to estimate the operating characteristics and their variability for certain types of ensemble methods. Bootstrapping a model can require a huge amount of work if the training data set is large. Fortunately in many cases the technique lets us determine the eff…
A new algorithm speeds up neural network training with less data.
We consider the performance of the bootstrap in high-dimensions for the setting of linear regression, where but 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…
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,…
An approximate method for conducting resampling in Lasso, the 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…
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…
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--…
New methods improve insurance data quality for catastrophic events.
A new clustering method using Bayesian techniques improves robustness and interpretability.
Private statistical inference methods improve confidence interval lengths.
Ribbon: Scalable Approximation and Robust Uncertainty Quantification
Regularizes attention scores in vision transformers using bootstrapping.
The paper studies how to use AI-generated labels in econometrics to avoid bias.
We develop and implement a novel fast bootstrap for dependent data. Our scheme is based on the i.i.d. resampling of the smoothed moment indicators. We characterize the class of parametric and semi-parametric estimation problems for which the method is valid. We show the asymptotic refinements of the proposed procedure,…
Efficiently clusters survival curves without computationally intensive resampling.
Proposes a new data augmentation method for imbalanced datasets in both classification and regression.
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…
New robust control method for uncertain systems using bootstrapped noise.
Non-parametric bootstrap improves robust portfolio and trading strategy optimization.
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…
SGD improves generalization by using gradient variability as a proxy for data randomness.
Develops a method to optimize hyperparameters for subsampling methods.
Investors in Target Date Funds are automatically switched from high risk to low risk assets as their retirements approach. Such funds have become very popular, but our analysis brings into question the rationale for them. Based on both a model with parameters fitted to historical returns and on bootstrap resampling, we…
A framework infers feature importance with uncertainties for high-dimensional data.
Probabilistic graphical models are graphical representations of probability distributions. Graphical models have applications in many fields including biology, social sciences, linguistic, neuroscience. In this paper, we propose directed acyclic graphs (DAGs) learning via bootstrap aggregating. The proposed procedure i…
New methods improve anomaly detection with reduced false positives.
New method speeds up uncertainty estimation for large datasets in causal inference.
The paper provides rigorous guarantees for m-out-of-n bootstrap estimators of sample quantiles.
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…