检索结果-内蒙古大学图书馆

arXiv 2024年

作者： Sun, Shangquan Ren, Wenqi Liu, Zikun Park, Hyunhee Wang, Rui Cao, Xiaochun Institute of Information Engineering Chinese Academy of Sciences Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China School of Cyber Science and Technology Shenzhen Campus of Sun Yat-sen University China Guangdong Provincial Key Laboratory of Information Security Technology China China Camera Innovation Group Samsung Electronics Korea Republic of

Image restoration has experienced significant advancements due to the development of deep learning. Nevertheless, it encounters challenges related to ill-posed problems, resulting in deviations between single model predictions and ground-truths. Ensemble learning, as a powerful machine learning technique, aims to address these deviations by combining the predictions of multiple base models. Most existing works adopt ensemble learning during the design of restoration models, while only limited research focuses on the inference-stage ensemble of pre-trained restoration models. Regression-based methods fail to enable efficient inference, leading researchers in academia and industry to prefer averaging as their choice for post-training ensemble. To address this, we reformulate the ensemble problem of image restoration into Gaussian mixture models (GMMs) and employ an expectation maximization (EM)-based algorithm to estimate ensemble weights for aggregating prediction candidates. We estimate the range-wise ensemble weights on a reference set and store them in a lookup table (LUT) for efficient ensemble inference on the test set. Our algorithm is model-agnostic and training-free, allowing seamless integration and enhancement of various pre-trained image restoration models. It consistently outperforms regression-based methods and averaging ensemble approaches on 14 benchmarks across 3 image restoration tasks, including super-resolution, deblurring and deraining. The codes and all estimated weights have been released in Github. © 2024, CC BY.

关键词： expectation maximization algorithm

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Estimation and imputation of missing data in longitudinal models with Zero-Inflated Poisson response variable

arXiv

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arXiv 2024年

作者： Martinez-Lobo, D.S. Martinez, Oscar Orlando Melo Gutierrez, Nelson Alirio Cruz Universidad del Bosque Colombia Departamento de Estadistica Facultad de Ciencias Universidad Nacional de Colombia Colombia Universitat de les Illes Balears Departament de Matemàtiques i Informàtica Spain

This research deals with the estimation and imputation of missing data in longitu- dinal models with a Poisson response variable inflated with zeros. A methodology is proposed that is based on the use of maximum likelihood, assuming that data is missing at random and that there is a correlation between the response vari- ables. In each of the times, the expectation maximization (EM) algorithm is used: in step E, a weighted regression is carried out, conditioned on the previous times that are taken as covariates. In step M, the estimation and imputation of the missing data are performed. The good performance of the methodology in dif- ferent loss scenarios is demonstrated in a simulation study comparing the model only with complete data, and estimating missing data using the mode of the data of each individual. Furthermore, in a study related to the growth of corn, it is tested on real data to develop the algorithm in a practical scenario. © 2024, CC BY-NC-ND.

关键词： expectation maximization algorithm

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MissNODAG: Differentiable Learning of Cyclic Causal Graphs from Incomplete Data

arXiv

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arXiv 2024年

作者： Sethuraman, Muralikrishnna G. Nabi, Razieh Fekri, Faramarz Georgia Institute of Technology United States Emory University United States

Causal discovery in real-world systems, such as biological networks, is often complicated by feedback loops and incomplete data. Standard algorithms, which assume acyclic structures or fully observed data, struggle with these challenges. To address this gap, we propose MissNODAG, a differentiable framework for learning both the underlying cyclic causal graph and the missingness mechanism from partially observed data, including data missing not at random. Our framework integrates an additive noise model with an expectation-maximization procedure, alternating between imputing missing values and optimizing the observed data likelihood, to uncover both the cyclic structures and the missingness mechanism. We demonstrate the effectiveness of MissNODAG through synthetic experiments and an application to real-world gene perturbation data. © 2024, CC BY.

关键词： expectation maximization algorithm

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Improved Evolutionary algorithms for Submodular maximization with Cost Constraints

arXiv

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arXiv 2024年

作者： Zhu, Yanhui Basu, Samik Pavan, A. Department of Computer Science Iowa State University United States

We present an evolutionary algorithm evo-SMC for the problem of Submodular maximization under Cost constraints (SMC). Our algorithm achieves 1/2-approximation with a high probability 1 - 1/n within O(n2Kβ) iterations, where Kβ denotes the maximum size of a feasible solution set with cost constraint β. To the best of our knowledge, this is the best approximation guarantee offered by evolutionary algorithms for this problem. We further refine evo-SMC, and develop st-evo-SMC. This stochastic version yields a significantly faster algorithm while maintaining the approximation ratio of 1/2, with probability 1 - ϵ. The required number of iterations reduces to O(nKβ log (1/ϵ)/p), where the user defined parameters p ∈ (0, 1] represents the stochasticity probability, and ϵ ∈ (0, 1] denotes the error threshold. Finally, the empirical evaluations carried out through extensive experimentation substantiate the efficiency and effectiveness of our proposed algorithms. Our algorithms consistently outperform existing methods, producing higher-quality solutions. © 2024, CC BY.

关键词： expectation maximization algorithm

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OPTIMIZING BACKWARD POLICIES IN GFLOWNETS VIA TRAJECTORY LIKELIHOOD maximization

arXiv

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arXiv 2024年

作者： Gritsaev, Timofei Morozov, Nikita Samsonov, Sergey Tiapkin, Daniil HSE University Russia Constructor University Bremen Germany CMAP CNRS École polytechnique Institut Polytechnique de Paris France Université Paris-Saclay CNRS Laboratoire de mathématiques d’Orsay France

Generative Flow Networks (GFlowNets) are a family of generative models that learn to sample objects with probabilities proportional to a given reward function. The key concept behind GFlowNets is the use of two stochastic policies: a forward policy, which incrementally constructs compositional objects, and a backward policy, which sequentially deconstructs them. Recent results show a close relationship between GFlowNet training and entropy-regularized reinforcement learning (RL) problems with a particular reward design. However, this connection applies only in the setting of a fixed backward policy, which might be a significant limitation. As a remedy to this problem, we introduce a simple backward policy optimization algorithm that involves direct maximization of the value function in an entropy-regularized Markov Decision Process (MDP) over intermediate rewards. We provide an extensive experimental evaluation of the proposed approach across various benchmarks in combination with both RL and GFlowNet algorithms and demonstrate its faster convergence and mode discovery in complex environments. Copyright © 2024, The Authors. All rights reserved.

关键词： expectation maximization algorithm

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Unsupervised ensemble learning for genome sequencing

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PATTERN RECOGNITION 2022年 129卷

作者： Pages-Zamora, Alba Ochoa, Idoia Cavero, Gonzalo Ruiz Villalvilla-Ornat, Pol Univ Politecn Catalunya BarcelonaTech UPC SPCOM Grp C-Jordi Girona 31 Barcelona 08034 Spain Univ Navarra Tecnun Manuel Lardizabal 13 San Sebastian 20018 Spain Swiss Fed Inst Technol Erdbeobachtung u Fernerkundung HCP G 33-1Leopold Ruzicka Weg 4 CH-8093 Zurich Switzerland

Unsupervised ensemble learning refers to methods devised for a particular task that combine data pro-vided by decision learners taking into account their reliability, which is usually inferred from the data. Here, the variant calling step of the next generation sequencing technologies is formulated as an unsuper-vised ensemble classification problem. A variant calling algorithm based on the expectation-maximization algorithm is further proposed that estimates the maximum-a-posteriori decision among a number of classes larger than the number of different labels provided by the learners. Experimental results with real human DNA sequencing data show that the proposed algorithm is competitive compared to state-of -the-art variant callers as GATK, HTSLIB, and Platypus.(c) 2022 The Author(s). Published by Elsevier *** is an open access article under the CC BY-NC-ND license ( http://***/licenses/by-nc-nd/4.0/ )

关键词： expectation maximization algorithm Variant calling Genome sequencing Unsupervised multi-class ensemble classifier GATK

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Two-part models for assessing misrepresentation on risk status

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EUROPEAN ACTUARIAL JOURNAL 2021年第2期11卷 503-539页

作者： Chen, Li-Chieh Su, Jianxi Xia, Michelle Purdue Univ Dept Stat 150 N Univ St W Lafayette IN 47906 USA Northern Illinois Univ Dept Stat & Actuarial Sci De Kalb IL 60115 USA

Claims modeling is a classical actuarial task aimed to understand the claim distribution given a set of risk factors. Yet some risk factors may be subject to misrepresentation, giving rise to bias in the estimated risk effects. Motivated by the unique characteristics of real health insurance data, we propose a novel class of two-part aggregate loss models that can (a) account for the semi-continuous feature of aggregate loss data, (b) test and adjust for misrepresentation risk in insurance ratemaking, and (c) incorporate an arbitrary number of correctly measured risk factors. The unobserved status of misrepresentation is captured via a latent factor shared by the two regression models on the occurrence and size of aggregate losses. For the complex two-part model, we derive explicit iterative formulas for the expectation maximization algorithm adopted in parameter estimation. Analytical expressions are obtained for the observed Fisher information matrix, ensuring computational efficiency in large-sample inferences on risk effects. We perform extensive simulation studies to demonstrate the convergence and robustness of the estimators under model misspecification. We illustrate the practical usefulness of the models by two empirical applications based on real medical claims data.

关键词： expectation maximization algorithm Loss modeling Misrepresentation fraud Mixture models Semi-continuous data

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Nowcasting in triple-system estimation

arXiv

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arXiv 2024年

作者： Zult, Daan B. van der Heijden, Peter G.M. Bakker, Bart F.M. Statistics Netherlands Netherlands Utrecht University University of Southampton Netherlands Statistics Netherlands and VU University Amsterdam Netherlands

Multiple systems estimation uses samples that each cover part of a population to obtain a total population size estimate. Ideally, all the available samples are used, but if some samples are available (much) later, one may use only the samples that are available early. Under some regularity conditions, including sample independence, two samples is enough to obtain an asymptotically unbiased population size estimate. However, the assumption of sample independence may be unrealistic, especially when samples are derived from administrative sources. The sample independence assumption can be relaxed when three or more samples are used, which is therefore generally recommended. This may be a problem if the third sample is available much later than the first two samples. Therefore, in this paper we propose a new approach that deals with this issue by utilising older samples, using the so-called expectation maximisation algorithm. This leads to a population size nowcast estimate that is asymptotically unbiased under more relaxed assumptions than the estimate based on two samples. The resulting nowcasting model is applied to the problem of estimating the number of homeless people in The Netherlands, which leads to reasonably accurate nowcast estimates. © 2024, CC BY.

关键词： expectation maximization algorithm

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Statistical Finite Elements via Interacting Particle Langevin Dynamics

arXiv

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arXiv 2024年

作者： Glyn-Davies, Alex Duffin, Connor Kazlauskaite, Ieva Girolami, Mark Akyildiz, Ö. Deniz

In this paper, we develop a class of interacting particle Langevin algorithms to solve inverse problems for partial differential equations (PDEs). In particular, we leverage the statistical finite elements (statFEM) formulation to obtain a finite-dimensional latent variable statistical model where the parameter is that of the (discretised) forward map and the latent variable is the statFEM solution of the PDE which is assumed to be partially observed. We then adapt a recently proposed expectation-maximisation like scheme, interacting particle Langevin algorithm (IPLA), for this problem and obtain a joint estimation procedure for the parameters and the latent variables. We consider three main examples: (i) estimating the forcing for linear Poisson PDE, (ii) estimating the forcing for nonlinear Poisson PDE, and (iii) estimating diffusivity for linear Poisson PDE. We provide computational complexity estimates for forcing estimation in the linear case. We also provide comprehensive numerical experiments and preconditioning strategies that significantly improve the performance, showing that the proposed class of methods can be the choice for parameter inference in PDE models. Copyright © 2024, The Authors. All rights reserved.

关键词： expectation maximization algorithm

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Online Influence maximization with Semi-Bandit Feedback under Corruptions

arXiv

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arXiv 2024年

作者： Cheng, Xiaotong Nourani-Koliji, Behzad Maghsudi, Setareh The Department of Electrical Engineering and Information Technology Ruhr-University Bochum Bochum44801 Germany The Department of Computer Science University of Tübingen Tübingen72074 Germany The Department of Electrical Engineering and Information Technology Ruhr-University Bochum Bochum44801 Germany The Fraunhofer Heinrich Hertz Institute Berlin10587 Germany

In this work, we investigate the online influence maximization in social networks. Most prior research studies on online influence maximization assume that the nodes are fully cooperative and act according to their stochastically generated influence probabilities on others. In contrast, we study the online influence maximization problem in the presence of some corrupted nodes whose damaging effects diffuse throughout the network. We propose a novel bandit algorithm, CW-IMLinUCB, which robustly learns and finds the optimal seed set in the presence of corrupted users. Theoretical analyses establish that the regret performance of our proposed algorithm is better than the state-of-the-art online influence maximization algorithms. Extensive empirical evaluations on synthetic and real-world datasets also show the superior performance of our proposed algorithm. Copyright © 2024, The Authors. All rights reserved.

关键词： expectation maximization algorithm

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