arXiv research

Boosts change-point detection power with optimal sub-sampling.

Optimal sub-sampling method improves statistical model performance.

Active covariance estimation using random sub-sampling of variable subsets.

Paper analyzes Nyström regularization for time series forecasting with sequential sub-sampling.

LOUPE optimizes MRI sub-sampling patterns using machine learning.

Efficient bandit exploration for various distributions without distribution-specific tuning.

Many data-fitting applications require the solution of an optimization problem involving a sum of large number of functions of high dimensional parameter. Here, we consider the problem of minimizing a sum of $n$ functions over a convex constraint set $\mathcal{X} \subseteq \mathbb{R}^{p}$ where both $n$ and $p$ are lar…

Large scale optimization problems are ubiquitous in machine learning and data analysis and there is a plethora of algorithms for solving such problems. Many of these algorithms employ sub-sampling, as a way to either speed up the computations and/or to implicitly implement a form of statistical regularization. In this …

This study addresses the challenges of dynamic mini-batch sub-sampling in neural network training.

This paper shows using sub-sample estimates can improve optimization results in large-scale problems.

Deep learning reduces data rate for clearer ultrasound images.

A new method uses sub-sampling to make cubic regularization feasible for large-scale learning.

New method uses PDMPs with sub-sampling for efficient sampling from posterior distributions.

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

We consider the problem of minimizing a sum of $n$ functions over a convex parameter set $\mathcal{C} \subset \mathbb{R}^p$ where $n\gg p\gg 1$. In this regime, algorithms which utilize sub-sampling techniques are known to be effective. In this paper, we use sub-sampling techniques together with low-rank approximation …

Paper introduces efficient online sub-sampling for RL with function approximation, reducing policy updates.

The standard approach to compressive sampling considers recovering an unknown deterministic signal with certain known structure, and designing the sub-sampling pattern and recovery algorithm based on the known structure. This approach requires looking for a good representation that reveals the signal structure, and sol…

New methods for non-convex optimization using inexact Hessian approximations.

Improved statistical computation through efficient matrix sampling.

Efficiently approximates statistical leverage scores for faster KRR.

Study sharpens threshold for matching correlated graphs without labels.

Adapts to high dimensions for estimating conditional moments.

Improved MCMC for rare events in hidden Markov models.

Bayesian realized EGARCH models improve tail risk forecasting.

Bayesian realized-GARCH models forecast financial tail risks using two-sided Weibull distribution.

Corrects bias in random sampling matrices for improved ML methods.

For data sets populated by a very well modeled process and by another process of unknown probability density function (PDF), a desired feature when manipulating the fraction of the unknown process (either for enhancing it or suppressing it) consists in avoiding to modify the kinematic distributions of the well modeled …

StreaMRAK improves KRR for streaming data.

End-to-end analysis of SGD for STL with adaptive sub-sampling.

Paper proposes an efficient bandit-based algorithm for hyperparameter optimization.

We propose a randomized second-order method for optimization known as the Newton Sketch: it is based on performing an approximate Newton step using a randomly projected or sub-sampled Hessian. For self-concordant functions, we prove that the algorithm has super-linear convergence with exponentially high probability, wi…

Granger causality reviewed and advanced for complex data.

A novel distributed adaptive NN classifier for large data sets.

This paper enhances stability selection by evaluating overall results robustness and identifying optimal regularization values.

This paper compares and analyzes random projections and column sub-sampling for dimension reduction in regression.

New framework estimates VaR and ES jointly using Bayesian methods.

A deep learning beamformer improves ultrasound imaging quality across varying conditions.

Under-bagging $k$-NN improves performance on imbalanced classification.

Improved tail risk forecasting using realized measures and Bayesian methods.

Novel Newton method for large-scale kernel methods using random features.

A new algorithm selects data subsets avoiding outliers and high leverage points.

A new method reduces computational cost for gene expression inference in large microarray data sets.

This paper tackles efficient and scalable estimation of a complex model involving stochastic linear combinations of non-linear regressions.

Proposes $\alpha$-integration pooling for CNNs to improve performance.

This paper proposes an improved active learning method using classification trees.

New optimization method speeds up learning from data.

Study examines line search approximations for neural networks using MBSS.

BDMBC clusters data with varying densities using a new PLLS measure.