arXiv research

We analyze SGAs for statistical inference via asymptotics, improving tuning methods.

Paper speeds up boosting algorithms with less memory.

We consider large scale empirical risk minimization (ERM) problems, where both the problem dimension and variable size is large. In these cases, most second order methods are infeasible due to the high cost in both computing the Hessian over all samples and computing its inverse in high dimensions. In this paper, we pr…

Predictive models ground many state-of-the-art developments in statistical brain image analysis: decoding, MVPA, searchlight, or extraction of biomarkers. The principled approach to establish their validity and usefulness is cross-validation, testing prediction on unseen data. Here, I would like to raise awareness on e…

New method compresses large sample data for faster discriminant analysis.

DAL improves active learning for neural networks with large batch sizes.

Estimates sample size for subgroup analysis in randomized experiments.

Constraint-based causal discovery (CCD) algorithms require fast and accurate conditional independence (CI) testing. The Kernel Conditional Independence Test (KCIT) is currently one of the most popular CI tests in the non-parametric setting, but many investigators cannot use KCIT with large datasets because the test sca…

Bayesian LOO-CV method speeds up model evaluation for big data.

Most real-world networks are too large to be measured or studied directly and there is substantial interest in estimating global network properties from smaller sub-samples. One of the most important global properties is the number of vertices/nodes in the network. Estimating the number of vertices in a large network i…

LoCoV reduces portfolio optimization errors from sample covariance matrices.

Median-of-means sampling outperforms mean-of-means for large sample sizes in numerical integration.

Efficiently estimate risk of large portfolios using MLMC and sub-sampling.

A novel distributed adaptive NN classifier for large data sets.

Study non-monotonic loss functions in CRC, achieving valid risk control with large calibration samples.

Algorithm recovers large causal tree from small samples.

Sample size determination for a data set is an important statistical process for analyzing the data to an optimum level of accuracy and using minimum computational work. The applications of this process are credible in every domain which deals with large data sets and high computational work. This study uses Bayesian a…

High-dimensional data models, often with low sample size, abound in many interdisciplinary studies, genomics and large biological systems being most noteworthy. The conventional assumption of multinormality or linearity of regression may not be plausible for such models which are likely to be statistically complex due …

Improved KG completion on large datasets using entity-relation pair occurrences.

When can reliable inference be drawn in the "Big Data" context? This paper presents a framework for answering this fundamental question in the context of correlation mining, with implications for general large scale inference. In large scale data applications like genomics, connectomics, and eco-informatics the dataset…

Study finds that only a fraction of data is needed for accurate patient-level prediction models.

New convergence results for NGVI with various step sizes and sample sizes.

The paper improves methods for estimating set size using samples.

Study shows no degradation in neural network performance with larger batch sizes.

A new test improves statistical inference in bandit algorithms without sacrificing adaptiveness.

Bob predicts a future observation based on a sample of size one. Alice can draw a sample of any size before issuing her prediction. How much better can she do than Bob? Perhaps surprisingly, under a large class of loss functions, which we refer to as the Cover-Hart family, the best Alice can do is to halve Bob's risk. …

The paper shows how to efficiently generate large Gaussian process samples with reliability guarantees.

New auditors assess $f$-DP privacy with adaptive sampling, avoiding large sample sizes.

New summary measures reveal geometric structure in weighted measures on manifolds.

Uniform sampling of modest size is a coreset for regularized loss minimization.

Deep learning speeds up IFA estimation for large datasets.

This paper compares communication efficiency of split learning and federated learning in various scenarios.

pmsims R package uses Gaussian process for flexible sample size estimation in clinical models.

Adaptive scan Gibbs sampler improves large-scale inference performance.

We consider the problem of providing nonparametric confidence guarantees for undirected graphs under weak assumptions. In particular, we do not assume sparsity, incoherence or Normality. We allow the dimension $D$ to increase with the sample size $n$. First, we prove lower bounds that show that if we want accurate infe…

A new method improves active learning for large batch sizes.

The study proves sampling-based GNNs can approximate training on full graphs with small subgraphs.

Image denoising is an important pre-processing step in medical image analysis. Different algorithms have been proposed in past three decades with varying denoising performances. More recently, having outperformed all conventional methods, deep learning based models have shown a great promise. These methods are however …

New method improves uncertainty quantification for large batch sizes and misspecified models.

In the information-based paradigm of inference, model selection is performed by selecting the candidate model with the best estimated predictive performance. The success of this approach depends on the accuracy of the estimate of the predictive complexity. In the large-sample-size limit of a regular model, the predicti…

Sharp concentration bounds for i.i.d. variables.

The contour map of estimation error of Expected Shortfall (ES) is constructed. It allows one to quantitatively determine the sample size (the length of the time series) required by the optimization under ES of large institutional portfolios for a given size of the portfolio, at a given confidence level and a given esti…

Poisson Midpoint Method improves Langevin Dynamics for diffusion models.

We present a novel Metropolis-Hastings method for large datasets that uses small expected-size minibatches of data. Previous work on reducing the cost of Metropolis-Hastings tests yield variable data consumed per sample, with only constant factor reductions versus using the full dataset for each sample. Here we present…

Estimates neural representation dimensionality from small sample sizes.

Study on kernel regression risk in high dimensions using Pinsker bound.

Deep networks and forests perform differently with small samples.

For spherically symmetric distributions, efficient quantisation can be achieved with moderate sample sizes.