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Regression forests have long delivered state-of-the-art accuracy, often outperforming regression trees and even neural networks, but they suffer from limited interpretability as ensemble methods. In this work, we revisit forest pruning, an…

Machine Learning · Statistics 2025-03-10 Albert Dorador

The era of huge data necessitates highly efficient machine learning algorithms. Many common machine learning algorithms, however, rely on computationally intensive subroutines that are prohibitively expensive on large datasets. Oftentimes,…

Machine Learning · Computer Science 2023-09-26 Mo Tiwari

Supervised learning under measurement constraints is a common challenge in statistical and machine learning. In many applications, despite extensive design points, acquiring responses for all points is often impractical due to resource…

Methodology · Statistics 2025-03-19 Lin Wang

In the realm of contemporary data analysis, the use of massive datasets has taken on heightened significance, albeit often entailing considerable demands on computational time and memory. While a multitude of existing works offer optimal…

Methodology · Statistics 2024-06-21 Tal Agassi , Nir Keret , Malka Gorfine

We consider the challenges that arise when fitting complex ecological models to 'large' data sets. In particular, we focus on random effect models which are commonly used to describe individual heterogeneity, often present in ecological…

Methodology · Statistics 2022-05-17 Ruth King , Blanca Sarzo , Víctor Elvira

Several strategies have been developed recently to ensure valid inference after model selection; some of these are easy to compute, while others fare better in terms of inferential power. In this paper, we consider a selective inference…

Methodology · Statistics 2022-07-13 Snigdha Panigrahi , Jonathan Taylor

Non-probability sampling, for example in the form of online panels, has become a fast and cheap method to collect data. While reliable inference tools are available for classical probability samples, non-probability samples can yield…

Methodology · Statistics 2022-04-05 Gerhard Tutz

The average properties of the well-known Subset Sum Problem can be studied by the means of its randomised version, where we are given a target value $z$, random variables $X_1, \ldots, X_n$, and an error parameter $\varepsilon > 0$, and we…

We introduce an innovative approach to enhancing the empirical risk minimization (ERM) process in model training through a refined reweighting scheme of the training data to enhance fairness. This scheme aims to uphold the sufficiency rule…

Machine Learning · Computer Science 2024-10-02 Xuan Zhao , Klaus Broelemann , Salvatore Ruggieri , Gjergji Kasneci

To deal with very large datasets a mini-batch version of the Monte Carlo Markov Chain Stochastic Approximation Expectation-Maximization algorithm for general latent variable models is proposed. For exponential models the algorithm is shown…

Computation · Statistics 2023-08-30 Tabea Rebafka , Estelle Kuhn , Catherine Matias

We propose a general method for distributed Bayesian model choice, using the marginal likelihood, where a data set is split in non-overlapping subsets. These subsets are only accessed locally by individual workers and no data is shared…

Computation · Statistics 2022-10-18 Alexander Buchholz , Daniel Ahfock , Sylvia Richardson

We introduce a very general method for sparse and large-scale variable selection. The large-scale regression settings is such that both the number of parameters and the number of samples are extremely large. The proposed method is based on…

Statistics Theory · Mathematics 2019-07-31 Jelena Bradic

Random-effects models are frequently used to synthesise information from different studies in meta-analysis. While likelihood-based inference is attractive both in terms of limiting properties and of implementation, its application in…

Methodology · Statistics 2018-02-16 Ioannis Kosmidis , Annamaria Guolo , Cristiano Varin

Massive sized survival datasets are becoming increasingly prevalent with the development of the healthcare industry. Such datasets pose computational challenges unprecedented in traditional survival analysis use-cases. A popular way for…

Methodology · Statistics 2023-05-09 Nir Keret , Malka Gorfine

Learning the minimum/maximum mean among a finite set of distributions is a fundamental sub-task in planning, game tree search and reinforcement learning. We formalize this learning task as the problem of sequentially testing how the minimum…

Machine Learning · Statistics 2018-06-05 Emilie Kaufmann , Wouter Koolen , Aurelien Garivier

Data augmentation is an effective technique to improve the generalization of deep neural networks. However, previous data augmentation methods usually treat the augmented samples equally without considering their individual impacts on the…

Machine Learning · Computer Science 2021-03-17 Mingyang Yi , Lu Hou , Lifeng Shang , Xin Jiang , Qun Liu , Zhi-Ming Ma

Importance sampling is often used in machine learning when training and testing data come from different distributions. In this paper we propose a new variant of importance sampling that can reduce the variance of importance sampling-based…

Machine Learning · Computer Science 2016-11-11 Philip S. Thomas , Emma Brunskill

In analyzing big data for finite population inference, it is critical to adjust for the selection bias in the big data. In this paper, we propose two methods of reducing the selection bias associated with the big data sample. The first…

Methodology · Statistics 2019-01-08 Jae Kwang Kim , Zhonglei Wang

Large-scale multiple testing is a fundamental problem in high dimensional statistical inference. It is increasingly common that various types of auxiliary information, reflecting the structural relationship among the hypotheses, are…

Methodology · Statistics 2021-10-07 Hongyuan Cao , Jun Chen , Xianyang Zhang

As an alternative to the traditional sampling theory, compressed sensing allows acquiring much smaller amount of data, still estimating the spectra of frequency-sparse signals accurately. However, compressed sensing usually requires random…

Information Theory · Computer Science 2016-07-22 Shan Huang , Hong Sun , Haijian Zhang , Lei Yu
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