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Dense subgraph extraction is a fundamental problem in graph analysis and data mining, aimed at identifying cohesive and densely connected substructures within a given graph. It plays a crucial role in various domains, including social…

Data Structures and Algorithms · Computer Science 2024-03-01 Chia-Yang Hung , Chih-Ya Shen

In high-dimensional generalized linear models, it is crucial to identify a sparse model that adequately accounts for response variation. Although the best subset section has been widely regarded as the Holy Grail of problems of this type,…

Machine Learning · Statistics 2023-08-02 Junxian Zhu , Jin Zhu , Borui Tang , Xuanyu Chen , Hongmei Lin , Xueqin Wang

Dimensionality reduction is a fundamental task in modern data science. Several projection methods specifically tailored to take into account the non-linearity of the data via local embeddings have been proposed. Such methods are often based…

Machine Learning · Statistics 2026-01-28 Antonio Di Noia , Federico Ravenda , Antonietta Mira

The joint optimization of the reconstruction and classification error is a hard non convex problem, especially when a non linear mapping is utilized. In order to overcome this obstacle, a novel optimization strategy is proposed, in which a…

Machine Learning · Computer Science 2022-11-07 Ioannis A. Nellas , Sotiris K. Tasoulis , Vassilis P. Plagianakos , Spiros V. Georgakopoulos

Line spectral estimation theory aims to estimate the off-the-grid spectral components of a time signal with optimal precision. Recent results have shown that it is possible to recover signals having sparse line spectra from few temporal…

Information Theory · Computer Science 2017-01-31 Maxime Ferreira Da Costa , Wei Dai

In this paper, the problem of distributed optimization is studied via a network of agents. Each agent only has access to a stochastic gradient of its own objective function in the previous time, and can communicate with its neighbors via a…

Optimization and Control · Mathematics 2024-01-29 Yuchen Yang , Kaihong Lu , Long Wang

We investigate the feature compression of high-dimensional ridge regression using the optimal subsampling technique. Specifically, based on the basic framework of random sampling algorithm on feature for ridge regression and the A-optimal…

Computation · Statistics 2022-04-19 Hanyu Li , Chengmei Niu

We consider the problem of model selection type aggregation in the context of density estimation. We first show that empirical risk minimization is sub-optimal for this problem and it shares this property with the exponential weights…

Statistics Theory · Mathematics 2016-09-29 Pierre C. Bellec

Linear optimization problems are investigated whose parameters are uncertain. We apply coherent distortion risk measures to capture the possible violation of a restriction. Each risk constraint induces an uncertainty set of coefficients,…

Methodology · Statistics 2017-12-18 Karl Mosler , Pavel Bazovkin

This paper presents a brand new nonparametric density estimation strategy named the best-scored random forest density estimation whose effectiveness is supported by both solid theoretical analysis and significant experimental performance.…

Machine Learning · Statistics 2019-05-10 Hanyuan Hang , Hongwei Wen

Unbiased and consistent variance estimators generally do not exist for design-based treatment effect estimators because experimenters never observe more than one potential outcome for any unit. The problem is exacerbated by interference and…

Methodology · Statistics 2024-07-04 Christopher Harshaw , Joel A. Middleton , Fredrik Sävje

The robust estimator presented in this paper processes each structure independently. The scales of the structures are estimated adaptively and no threshold is involved in spite of different objective functions. The user has to specify only…

Computer Vision and Pattern Recognition · Computer Science 2017-04-21 Xiang Yang , Peter Meer

In this paper, we consider matrix completion with absolute deviation loss and obtain an estimator of the median matrix. Despite several appealing properties of median, the non-smooth absolute deviation loss leads to computational challenge…

Machine Learning · Statistics 2020-06-19 Weidong Liu , Xiaojun Mao , Raymond K. W. Wong

Random sampling is an essential tool in the processing and transmission of data. It is used to summarize data too large to store or manipulate and meet resource constraints on bandwidth or battery power. Estimators that are applied to the…

Databases · Computer Science 2015-03-19 Edith Cohen , Haim Kaplan

High-dimensional data poses unique challenges in outlier detection process. Most of the existing algorithms fail to properly address the issues stemming from a large number of features. In particular, outlier detection algorithms perform…

Machine Learning · Computer Science 2020-09-22 Firuz Kamalov , Ho Hon Leung

This article proposes a novel density estimation based algorithm for carrying out supervised machine learning. The proposed algorithm features O(n) time complexity for generating a classifier, where n is the number of sampling instances in…

Machine Learning · Statistics 2007-11-06 Yen-Jen Oyang , Chien-Yu Chen , Darby Tien-Hao Chang , Chih-Peng Wu

We consider a linear model where the coefficients - intercept and slopes - are random with a law in a nonparametric class and independent from the regressors. Identification often requires the regressors to have a support which is the whole…

Statistics Theory · Mathematics 2020-06-22 Christophe Gaillac , Eric Gautier

Convergence rates of kernel density estimators for stationary time series are well studied. For invertible linear processes, we construct a new density estimator that converges, in the supremum norm, at the better, parametric, rate…

Statistics Theory · Mathematics 2009-09-29 Anton Schick , Wolfgang Wefelmeyer

Data subject to heavy-tailed errors are commonly encountered in various scientific fields, especially in the modern era with explosion of massive data. To address this problem, procedures based on quantile regression and Least Absolute…

Statistics Theory · Mathematics 2014-10-09 Jianqing Fan , Quefeng Li , Yuyan Wang

We propose a minimum distance estimation method for robust regression in sparse high-dimensional settings. The traditional likelihood-based estimators lack resilience against outliers, a critical issue when dealing with high-dimensional…

Methodology · Statistics 2013-07-12 Aurélie C. Lozano , Nicolai Meinshausen
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