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Network pruning is a promising way to generate light but accurate models and enable their deployment on resource-limited edge devices. However, the current state-of-the-art assumes that the effective sub-network and the other superfluous…

Computer Vision and Pattern Recognition · Computer Science 2022-12-08 Yingchun Wang , Song Guo , Jingcai Guo , Weizhan Zhang , Yida Xu , Jie Zhang , Yi Liu

Missing data are frequently encountered in high-dimensional problems, but they are usually difficult to deal with using standard algorithms, such as the expectation-maximization (EM) algorithm and its variants. To tackle this difficulty,…

Methodology · Statistics 2018-02-08 Faming Liang , Bochao Jia , Jingnan Xue , Qizhai Li , Ye Luo

This paper proposes two linear projection methods for supervised dimension reduction using only the first and second-order statistics. The methods, each catering to a different parameter regime, are derived under the general Gaussian model…

Information Theory · Computer Science 2024-08-13 Biao Chen , Joshua Kortje

A loss function measures the discrepancy between the true values (observations) and their estimated fits, for a given instance of data. A loss function is said to be proper (unbiased, Fisher consistent) if the fits are defined over a unit…

Information Theory · Computer Science 2018-05-11 Amichai Painsky , Gregory W. Wornell

A kernel method for estimating a probability density function (pdf) from an i.i.d. sample drawn from such density is presented. Our estimator is a linear combination of kernel functions, the coefficients of which are determined by a linear…

Statistics Theory · Mathematics 2023-04-20 Yoshihito Kazashi , Fabio Nobile

In many fields of science, generalized likelihood ratio tests are established tools for statistical inference. At the same time, it has become increasingly common that a simulator (or generative model) is used to describe complex processes…

Applications · Statistics 2016-03-21 Kyle Cranmer , Juan Pavez , Gilles Louppe

While robust parameter estimation has been well studied in parametric density estimation, there has been little investigation into robust density estimation in the nonparametric setting. We present a robust version of the popular kernel…

Machine Learning · Statistics 2014-11-18 Robert A. Vandermeulen , Clayton D. Scott

We present a new lower bound on the differential entropy rate of stationary processes whose sequences of probability density functions fulfill certain regularity conditions. This bound is obtained by showing that the gap between the…

Information Theory · Computer Science 2017-08-30 Meik Dörpinghaus

Although the $p_1$ model was proposed 40 years ago, little progress has been made to address asymptotic theories in this model, that is, neither consistency of the maximum likelihood estimator (MLE) nor other parameter estimation with…

Methodology · Statistics 2026-01-13 Qunqiang Feng , Yaru Tian , Ting Yan

Obtaining an accurate estimate of the underlying covariance matrix from finite sample size data is challenging due to sample size noise. In recent years, sophisticated covariance-cleaning techniques based on random matrix theory have been…

Computation · Statistics 2024-11-11 Christian Bongiorno , Lamia Lamrani

In statistical classification/multiple hypothesis testing and machine learning, a model distribution estimated from the training data is usually applied to replace the unknown true distribution in the Bayes decision rule, which introduces a…

Information Theory · Computer Science 2024-09-24 Zijian Yang , Vahe Eminyan , Ralf Schlüter , Hermann Ney

We present a class of algorithms capable of directly training deep neural networks with respect to large families of task-specific performance measures such as the F-measure and the Kullback-Leibler divergence that are structured and…

Machine Learning · Statistics 2021-09-22 Amartya Sanyal , Pawan Kumar , Purushottam Kar , Sanjay Chawla , Fabrizio Sebastiani

Accelerated algorithms for maximum likelihood image reconstruction are essential for emerging applications such as 3D tomography, dynamic tomographic imaging, and other high dimensional inverse problems. In this paper, we introduce and…

Computation · Statistics 2012-01-31 Stéphane Chrétien , Alfred O. Hero

Various problems in Engineering and Statistics require the computation of the likelihood ratio function of two probability densities. In classical approaches the two densities are assumed known or to belong to some known parametric family.…

Signal Processing · Electrical Eng. & Systems 2019-11-06 George V. Moustakides , Kalliopi Basioti

Optimum designs for parameter estimation in generalized regression models are standardly based on the Fisher information matrix (cf. Atkinson et al (2014) for a recent exposition). The corresponding optimality criteria are related to the…

Statistics Theory · Mathematics 2015-07-28 Katarína Burclová , Andrej Pázman

In this paper, we propose some estimators for the parameters of a statistical model based on Kullback-Leibler divergence of the survival function in continuous setting. We prove that the proposed estimators are subclass of "generalized…

Statistics Theory · Mathematics 2016-07-01 Yaser Mehrali , Majid Asadi

Expectation maximization (EM) is the default algorithm for fitting probabilistic models with missing or latent variables, yet we lack a full understanding of its non-asymptotic convergence properties. Previous works show results along the…

Machine Learning · Computer Science 2022-03-01 Frederik Kunstner , Raunak Kumar , Mark Schmidt

One of the fundamental problems in machine learning is the estimation of a probability distribution from data. Many techniques have been proposed to study the structure of data, most often building around the assumption that observations…

Machine Learning · Statistics 2013-02-22 Oren Rippel , Ryan Prescott Adams

We introduce estimation and test procedures through divergence minimization for models satisfying linear constraints with unknown parameter. Several statistical examples and motivations are given. These procedures extend the empirical…

Statistics Theory · Mathematics 2008-11-24 Michel Broniatowski , Amor Keziou

This study tackles the efficient estimation of Kullback-Leibler (KL) Divergence in Dirichlet Mixture Models (DMM), crucial for clustering compositional data. Despite the significance of DMMs, obtaining an analytically tractable solution for…

Machine Learning · Statistics 2024-03-20 Samyajoy Pal , Christian Heumann