arXiv research

Learnable multiclass hypothesis classes don't always have a sample compression scheme of fixed size.

Support vector regression (SVR) has been widely used to reduce the high computational cost of computer simulation. SVR assumes the input parameters have equal sample sizes, but unequal sample sizes are often encountered in engineering practices. To solve this issue, a new prediction approach based on SVR, namely as hig…

In biospectroscopy, suitably annotated and statistically independent samples (e. g. patients, batches, etc.) for classifier training and testing are scarce and costly. Learning curves show the model performance as function of the training sample size and can help to determine the sample size needed to train good classi…

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

Adapts DR objectives for both sample and feature size reduction.

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

We obtain the first positive results for bounded sample compression in the agnostic regression setting with the $\ell_p$ loss, where $p\in [1,\infty]$. We construct a generic approximate sample compression scheme for real-valued function classes exhibiting exponential size in the fat-shattering dimension but independen…

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

Leveraging reference-only samples for two-sample testing under size asymmetry

Polyak step size GD reaches final radius of convergence after log iterations.

New method for MMD with unequal sample sizes improves test power.

A new method calculates intrinsic effective sample size for manifold-valued data.

Estimates sample size for subgroup analysis in randomized experiments.

Paper overcomes sample size barrier in reinforcement learning with generative models.

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…

The paper improves methods for estimating set size using samples.

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

Study ridge ensembles in proportional feature-to-sample size regime, proving risk equivalence and GCV consistency.

Privacy affects how much data is needed for CVaR optimization.

The paper introduces the concept of a cluster structure to define a joint distribution of the sample size and its exchangeable random partitions. The cluster structure allows the probability distribution of the random partitions of a subset of the sample to be dependent on the sample size, a feature not presented in a …

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

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. …

Exact distribution of split conformal prediction coverage found.

Unified Bayesian model for multi-modal, small sample size biomedical data classification.

Kurtosis is seen as a measure of the discrepancy between the observed data and a Gaussian distribution and is defined when the 4th moment is finite. In this work an empirical study is conducted to investigate the behaviour of the sample estimate of kurtosis with respect to sample size and the tail index when applied to…

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

Downsampling can improve generalization in ridgeless linear regression, especially with optimal sketching size.

This paper explores how effective sample size, dimensionality, and model performance are related in covariate shift adaptation.

Regularized EM algorithm improves clustering performance with small sample sizes.

Avare improves optimization and sampling with adaptive importance sampling.

We consider the least-squares regression problem and provide a detailed asymptotic analysis of the performance of averaged constant-step-size stochastic gradient descent (a.k.a. least-mean-squares). In the strongly-convex case, we provide an asymptotic expansion up to explicit exponentially decaying terms. Our analysis…

Reduces multiclass and regression compression schemes to binary ones.

The paper improves A/B testing for non-Gaussian data, ensuring reliable results with large sample sizes.

The paper analyzes convergence of neural SDEs as sample size increases.

It was proved in 1998 by Ben-David and Litman that a concept space has a sample compression scheme of size d if and only if every finite subspace has a sample compression scheme of size d. In the compactness theorem, measurability of the hypotheses of the created sample compression scheme is not guaranteed; at the same…

Differentiable learning via SGD and GD can simulate various learning problems, depending on precision and minibatch size.

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…

This paper examines the convergence of adaptive sampling methods for Bayesian neural networks.

Delayed rejection HMC improves sampling efficiency for multiscale distributions.

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…

Alignment of neural network representations is influenced by SNR and sample size.

Transforms any test into anytime-valid with sample savings.

New method reduces ensemble size for linear bandits, achieving near optimal regret.

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…

State-of-the-art implementations of boosting, such as XGBoost and LightGBM, can process large training sets extremely fast. However, this performance requires that the memory size is sufficient to hold a 2-3 multiple of the training set size. This paper presents an alternative approach to implementing the boosted trees…

We introduce and develop a novel approach to outlier detection based on adaptation of random subspace learning. Our proposed method handles both high-dimension low-sample size and traditional low-dimensional high-sample size datasets. Essentially, we avoid the computational bottleneck of techniques like minimum covaria…

Estimates neural representation dimensionality from small sample sizes.

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