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Related papers: Entropic Trace Estimates for Log Determinants

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We study the problem of estimating from data, a sparse approximation to the inverse covariance matrix. Estimating a sparsity constrained inverse covariance matrix is a key component in Gaussian graphical model learning, but one that is…

Machine Learning · Statistics 2011-06-28 Suvrit Sra , Dongmin Kim

We propose a generic Markov Chain Monte Carlo (MCMC) algorithm to speed up computations for datasets with many observations. A key feature of our approach is the use of the highly efficient difference estimator from the survey sampling…

Methodology · Statistics 2017-08-03 Matias Quiroz , Mattias Villani , Robert Kohn

Optimal sensor placement enhances the efficiency of a variety of applications for monitoring dynamical systems. It has been established that deterministic solutions to the sensor placement problem are insufficient due to the many…

Systems and Control · Electrical Eng. & Systems 2023-03-20 Amin Jabini , Erik A. Johnson

Optimal experimental designs are probability measures with finite support enjoying an optimality property for the computation of least squares estimators. We present an algorithm for computing optimal designs on finite sets based on the…

Numerical Analysis · Mathematics 2022-01-11 Federico Piazzon

In this letter we propose the use of physics techniques for entropy determination on constrained parameter optimization problems. The main feature of such techniques, the construction of an unbiased walk on energy space, suggests their use…

Statistical Mechanics · Physics 2009-11-07 A. R. Lima , M. Argollo de Menezes

The implicit trace estimation problem asks for an approximation of the trace of a square matrix, accessed via matrix-vector products (matvecs). This paper designs new randomized algorithms, XTrace and XNysTrace, for the trace estimation…

Numerical Analysis · Mathematics 2024-01-09 Ethan N. Epperly , Joel A. Tropp , Robert J. Webber

There has been substantial progress in the inference of formal behavioural specifications from sample trajectories, for example, using Linear Temporal Logic (LTL). However, these techniques cannot handle specifications that correctly…

Logic in Computer Science · Computer Science 2025-05-20 Rajarshi Roy , Yash Pote , David Parker , Marta Kwiatkowska

Bayesian inference for Markov processes has become increasingly relevant in recent years. Problems of this type often have intractable likelihoods and prior knowledge about model rate parameters is often poor. Markov Chain Monte Carlo…

Computation · Statistics 2014-10-23 Jamie Owen , Darren J. Wilkinson , Colin S. Gillespie

In this paper, we present a new explainability formalism designed to shed light on how each input variable of a test set impacts the predictions of machine learning models. Hence, we propose a group explainability formalism for trained…

Machine Learning · Statistics 2022-08-12 François Bachoc , Fabrice Gamboa , Max Halford , Jean-Michel Loubes , Laurent Risser

The log Gaussian Cox process is a flexible class of point pattern models for capturing spatial and spatio-temporal dependence for point patterns. Model fitting requires approximation of stochastic integrals which is implemented through…

Computation · Statistics 2016-12-04 Shinichiro Shirota , Alan E. Gelfand

Maximum entropy (MAXENT) method has a large number of applications in theoretical and applied machine learning, since it provides a convenient non-parametric tool for estimating unknown probabilities. The method is a major contribution of…

Data Analysis, Statistics and Probability · Physics 2020-12-18 A. E. Allahverdyan , N. H. Martirosyan

Data used for analytics and machine learning often take the form of tables with categorical entries. We introduce a family of lossless compression algorithms for such data that proceed in four steps: $(i)$ Estimate latent variables…

Information Theory · Computer Science 2023-02-21 Andrea Montanari , Eric Weiner

In the paper, we introduce the maximum entropy estimator based on 2-dimensional empirical distribution of the observation sequence of hidden Markov model , when the sample size is big: in that case computing the maximum likelihood estimator…

Statistics Theory · Mathematics 2023-03-16 Shulan Hu , Xinyu Wang , Liming Wu

We present two new methods for estimating the order (memory depth) of a finite alphabet Markov chain from observation of a sample path. One method is based on entropy estimation via recurrence times of patterns, and the other relies on a…

Statistics Theory · Mathematics 2007-06-13 Yuval Peres , Paul Shields

Determinantal point processes (DPPs) are popular probabilistic models that arise in many machine learning tasks, where distributions of diverse sets are characterized by matrix determinants. In this paper, we develop fast algorithms to find…

Discrete Mathematics · Computer Science 2017-06-15 Insu Han , Prabhanjan Kambadur , Kyoungsoo Park , Jinwoo Shin

Incremental methods for structure learning of pairwise Markov random fields (MRFs), such as grafting, improve scalability by avoiding inference over the entire feature space in each optimization step. Instead, inference is performed over an…

Machine Learning · Computer Science 2018-05-22 Walid Chaabene , Bert Huang

We consider the problem of estimating the trace and diagonal entries of an N-order tensor (where $N \geq 2$) under the framework where the tensor can only be accessed through tensor-vector multiplication. The aim is to estimate the tensor's…

Numerical Analysis · Mathematics 2025-10-28 Bhisham Dev Verma , Rameshwar Pratap , Keegan Kang

When an expert operates a perilous dynamic system, ideal constraint information is tacitly contained in their demonstrated trajectories and controls. The likelihood of these demonstrations can be computed, given the system dynamics and task…

Systems and Control · Electrical Eng. & Systems 2021-02-26 David L. McPherson , Kaylene C. Stocking , S. Shankar Sastry

We present on-line policy gradient algorithms for computing the locally optimal policy of a constrained, average cost, finite state Markov Decision Process. The stochastic approximation algorithms require estimation of the gradient of the…

Optimization and Control · Mathematics 2018-12-18 Vikram Krishnamurthy , Felisa Vazquez Abad

We present a general framework for applying machine-learning algorithms to the verification of Markov decision processes (MDPs). The primary goal of these techniques is to improve performance by avoiding an exhaustive exploration of the…