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检索条件"主题词=Expectation maximization algorithm"

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Centralized Selection with Preferences in the Presence of Biases 41

Centralized Selection with Preferences in the Presence of Bi...

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41st International Conference on Machine Learning, ICML 2024

作者： Elisa Celis, L. Kumar, Amit Vishnoi, Nisheeth K. Xu, Andrew Yale University United States IIT Delhi India

This paper considers the scenario in which there are multiple institutions, each with a limited capacity for candidates, and candidates, each with preferences over the institutions. A central entity evaluates the utility of each candidate to the institutions, and the goal is to select candidates for each institution in a way that maximizes utility while also considering the candidates' preferences. The paper focuses on the setting in which candidates are divided into multiple groups and the observed utilities of candidates in some groups are biased-systematically lower than their true utilities. The first result is that, in these biased settings, prior algorithms can lead to selections with sub-optimal true utility and significant discrepancies in the fraction of candidates from each group that get their preferred choices. Subsequently, an algorithm is presented along with proof that it produces selections that achieve near-optimal group fairness with respect to preferences while also nearly maximizing the true utility under distributional assumptions. Further, extensive empirical validation of these results in real-world and synthetic settings, in which the distributional assumptions may not hold, are presented. Copyright 2024 by the author(s)

关键词： expectation maximization algorithm

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Parameter Estimation in DAGs from Incomplete Data via Optimal Transport 41

Parameter Estimation in DAGs from Incomplete Data via Optima...

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41st International Conference on Machine Learning, ICML 2024

作者： Vo, Vy Le, Trung Vuong, Tung-Long Zhao, He Bonilla, Edwin V. Phung, Dinh Monash University Australia CSIRO's Data61 Australia VinAI Research Viet Nam

Estimating the parameters of a probabilistic directed graphical model from incomplete data is a long-standing challenge. This is because, in the presence of latent variables, both the likelihood function and posterior distribution are intractable without assumptions about structural dependencies or model classes. While existing learning methods are fundamentally based on likelihood maximization, here we offer a new view of the parameter learning problem through the lens of optimal transport. This perspective licenses a general framework that operates on any directed graphs without making unrealistic assumptions on the posterior over the latent variables or resorting to variational approximations. We develop a theoretical framework and support it with extensive empirical evidence demonstrating the versatility and robustness of our approach. Across experiments, we show that not only can our method effectively recover the ground-truth parameters but it also performs comparably or better than competing baselines on downstream applications. Copyright 2024 by the author(s)

关键词： expectation maximization algorithm

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Safety Analysis of China's Strategic Material Maritime Transport Channel Based on Bayesian Network 14

Safety Analysis of China's Strategic Material Maritime Trans...

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14th International Conference on Information Science and Technology, ICIST 2024

作者： Chen, Xue Wang, Xin Yu, Qing Feng, Lingling Zheng, Yuping College of Navigation Dalian Maritime University Liaoning China College of Navigation Jimei Maritime University Fujian China China Institute of Marine Technology and Economy Beijing China China Waterborne Transport Research Institute Beijing China

ISBN: (纸本)9798350353334

Aiming at the influence of the maritime transport channel environment on the safety of maritime transport of strategic materials in China, a safety environment assessment model of the maritime transport channel based on the Bayesian network is proposed. First of all, fully considering the environmental impact factors of traditional maritime transport channels, and combining them with non-traditional factors such as shipping events and sudden public health events, an index system of environmental impact factors of maritime transport channels for strategic materials in China is constructed. Based on expert experience and historical data, a model of environmental factors for marine accident risk at key nodes of maritime transportation channels based on the Bayesian Network (BN) was proposed. At the same time, using the actual data of the key nodes of China's strategic materials maritime transport channel from 2012 to 2022, such as maritime casualties and other related environmental factors, the expectation maximization (EM) algorithm is used to compensate for the absence of data and adaptively learn the parameters to update the parameters of the conditional probability table. Finally, the sensitivity analysis of the environmental factors affecting the safety of sea lanes is carried out. It is verified that the BN model can effectively identify the environmental factors of sea transport channels, and can accurately quantify the importance of environmental factors to the safety of sea transport of strategic materials in China. The analysis results show that the channel environment is the main factor affecting the safety of China's strategic materials maritime transport channels. © 2024 IEEE.

关键词： expectation maximization algorithm

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Energy Efficiency in 5G Networks Using Gaussian Mixture Models

Energy Efficiency in 5G Networks Using Gaussian Mixture Mode...

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2024 IEEE International Conference on Communication, Computing and Signal Processing, IICCCS 2024

作者： Alsalami, Zaid Kasthuri, R. Sowmiya, S. Rani, Pooja Gupta, Deepak Kumar Gowtham Kumar Reddy, I.V. The Islamic University College Of Technical Engineering Department Of Computers Techniques Engineering Najaf Iraq The Islamic University Of Al Diwaniyah College Of Technical Engineering Department Of Computers Techniques Engineering Al Diwaniyah Iraq Sri Ramakrishna Engineering College Department of Mathematics Tamilnadu Coimbatore India Prince Shri Venkateshwara Padmavathy Engineering College Chennai India Chandigarh Group of Colleges Chandigarh Engineering College Department of Computer Science Engineering Punjab Mohali140307 India Ies College of Technology Department of Electronics & Communication Engineering M.P. Bhopal India Saveetha Institute of Medical and Technical Sciences Department of CSE-AI Tamilnadu Chennai India

ISBN: (纸本)9798350390759

In this research, a new approach for improving energy efficiency of the networks in the 5G context is presented: utilizing the Gaussian Mixture Models. The process includes acquiring data in the first step, training the model in the second step, and using the model to make real-time decisions to minimize resources and inefficient energy consumption in the last step. This includes the collection of data on context and complete flow details of the course of traffic passage as well as actions committed by users. The GMM is then trained using the expectation-maximization (EM) algorithm for developing the model and reinforcement learning for real-time decision-making on the developed model. In case studies analyzed, there is evidence of the enhancement of energy consumption by 14% on average absolute reduction in energy per traffic unit, by 11% of the average absolute increase in network throughput and by 49% of the mean absolute reduction of latency in comparison with baseline conditions. © 2024 IEEE.

关键词： expectation maximization algorithm

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Unveiling the Cycloid Trajectory of EM Iterations in Mixed Linear Regression 41

Unveiling the Cycloid Trajectory of EM Iterations in Mixed L...

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41st International Conference on Machine Learning, ICML 2024

作者： Luo, Zhankun Hashemi, Abolfazl School of Electrical and Computer Engineering Purdue University IN United States

We study the trajectory of iterations and the convergence rates of the expectation-maximization (EM) algorithm for two-component Mixed Linear Regression (2MLR). The fundamental goal of MLR is to learn the regression models from unlabeled observations. The EM algorithm finds extensive applications in solving the mixture of linear regressions. Recent results have established the super-linear convergence of EM for 2MLR in the noiseless and high SNR settings under some assumptions and its global convergence rate with random initialization has been affirmed. However, the exponent of convergence has not been theoretically estimated and the geometric properties of the trajectory of EM iterations are not well-understood. In this paper, first, using Bessel functions we provide explicit closed-form expressions for the EM updates under all SNR regimes. Then, in the noiseless setting, we completely characterize the behavior of EM iterations by deriving a recurrence relation at the population level and notably show that all the iterations lie on a certain cycloid. Based on this new trajectory-based analysis, we exhibit the theoretical estimate for the exponent of super-linear convergence and further improve the statistical error bound at the finite-sample level. Our analysis provides a new framework for studying the behavior of EM for Mixed Linear Regression. Copyright 2024 by the author(s)

关键词： expectation maximization algorithm

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Nonparametric Identification Modeling of Ship Manoeuvering Motion Based on Gaussian Mixture Model

Nonparametric Identification Modeling of Ship Manoeuvering M...

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2024 International Annual Conference on Complex Systems and Intelligent Science, CSIS-IAC 2024

作者： Jiao, Xuechao Ren, Junsheng Fan, Junhao Hu, Yu Dalian Maritime University Navigation College Dalian China

ISBN: (纸本)9798331504755

The dynamic simulation of the ship dynamics model using non-parametric identification can achieve high-precision simulation of the ship simulator and adaptive control of the intelligent *** order to reduce the error in the area with large changes and improve the recognition efficiency, this paper improves the traditional Gaussian mixture model (GMM) and proposes a non-parametric identification model based on the improved GMM to simulate and predict the motion state of the ship. The GMM is trained with multidimensional data such as hydrodynamics and rudder angle, the traditional initial model data is improved in sections, the expectation maximization (EM) algorithm is used for parameter optimization, and the improved model parameters are obtained by calculating the modified Gaussian mixture coefficients. The hydrodynamics of the ship are calculated by Gaussian mixture regression using the trained model and the predicted rudder angle, and the maneuvering motion state is solved by the Runge-Kutta method. The feasibility and effectiveness of this method are validated through experiments on the motion state of different rudder angles using turning test and zigzag test. © 2024 IEEE.

关键词： expectation maximization algorithm

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Agnostic Learning of Mixed Linear Regressions with EM and AM algorithms 41

Agnostic Learning of Mixed Linear Regressions with EM and AM...

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41st International Conference on Machine Learning, ICML 2024

作者： Ghosh, Avishek Mazumdar, Arya Centre for Machine Intelligence for Data Sciences Indian Institute of Technology Bombay India Halıcıoğlu Data Science Institute University of California San Diego United States

Mixed linear regression is a well-studied problem in parametric statistics and machine learning. Given a set of samples, tuples of covariates and labels, the task of mixed linear regression is to find a small list of linear relationships that best fit the samples. Usually it is assumed that the label is generated stochastically by randomly selecting one of two or more linear functions, applying this chosen function to the covariates, and potentially introducing noise to the result. In that situation, the objective is to estimate the ground-truth linear functions up to some parameter error. The popular expectation maximization (EM) and alternating minimization (AM) algorithms have been previously analyzed for this. In this paper, we consider the more general problem of agnostic learning of mixed linear regression from samples, without such generative models. In particular, we show that the AM and EM algorithms, under standard conditions of separability and good initialization, lead to agnostic learning in mixed linear regression by converging to the population loss minimizers, for suitably defined loss functions. In some sense, this shows the strength of AM and EM algorithms that converges to "optimal solutions" even in the absence of realizable generative models. Copyright 2024 by the author(s)

关键词： expectation maximization algorithm

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Closing the Data Gap: A Comparative Study of Missing Value Imputation algorithms in Time Series Datasets 1

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7th Computational Methods in Systems and Software, CoMeSySo 2023

作者： Dolatabadi, Sepideh Hassankhani Budinská, Ivana Behmaneshpour, Rafe Gatial, Emil Institute of Informatics Slovak Academy of Sciences Bratislava Slovakia Rahbar Farayand Arya Company Department of Maintenance Bratislava Slovakia

ISBN: (数字)9783031535529

ISBN: (纸本)9783031535512

The presence of missing values in time series datasets poses significant challenges for accurate data analysis and modeling. In this paper, we present a comparative study of missing value imputation algorithms applied to time series datasets collected from various sensors over a period of six months. The goal of this study is to bridge the data gap by effectively replacing missing values and assessing the performance of three common imputation algorithms for time series: K-Nearest Neighbors (KNN) imputer, expectation-maximization (EM), and Multiple Imputation by Chained Equations (MICE). To evaluate the performance of the imputation techniques, we employed Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE) as metrics. Through rigorous experimentation and analysis, we found that each algorithm exhibited varying degrees of effectiveness in handling missing values within the time series datasets. Our findings highlight the importance of choosing an appropriate imputation algorithm based on the characteristics of the dataset and the specific requirements of the analysis. The results also demonstrate the potential of the MICE imputer in closing the data gap and improving the accuracy of subsequent analyses on time series sensor data. Overall, this study provides valuable insights into the performance and suitability of different missing value imputation algorithms for time series datasets, facilitating better decision-making and enhancing the reliability of data-driven applications in various domains. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： expectation maximization algorithm

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Leveraging Quantum Artificial Intelligence for Intelligent Face Recognition on the Internet of Medical Things (IoMT) for Smart City Surveillance 7th

Leveraging Quantum Artificial Intelligence for Intelligent F...

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7th International Conference on Intelligent Computing and Optimization, ICO 2023

作者： Chengxi, Hong Rajawat, Anand Singh Goyal, S.B. Solanki, Ram Kumar Chengyi University College Jimei University Xiamen361021 China School of Computers Science & Engineering Sandip University Nashik422213 India Faculty of Information Technology City University Malaysia Petaling Jaya46100 Malaysia

ISBN: (纸本)9783031508868

Within the contemporary context of smart city infrastructures, the Internet of Medical Things (IoMT) has the capacity to instigate a revolution in urban monitoring. Despite recent advancements, conventional face recognition algorithms continue to encounter predicaments such as scalability, economy, and accuracy. In this scholarly article, we aim to explore the enhancement of face recognition techniques by incorporating Quantum Artificial Intelligence (AI), specifically QAEM, into the IoMT framework. Our research specifically focuses on the modus operandi of this integration. The amalgamation of edge devices for preliminary face identification and the utilization of quantum servers for advanced recognition facilitates this approach to deliver improved recognition accuracy, reduced calculation times, and heightened scalability, while also improving the ease of scalability. Our findings emphasize the potential of QAEM as both a standard for future quantum-enhanced applications in the realm of smart cities and as a tool for ameliorated surveillance in those same cities. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： expectation maximization algorithm

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Temporal Elections: Welfare, Strategyproofness, and Proportionality 27

Temporal Elections: Welfare, Strategyproofness, and Proporti...

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27th European Conference on Artificial Intelligence, ECAI 2024

作者： Elkind, Edith Neoh, Tzeh Yuan Teh, Nicholas University of Oxford United Kingdom Alan Turing Insitute United Kingdom Agency for Science Technology and Research Singapore

ISBN: (纸本)9781643685489

We investigate a model of sequential decision-making where a single alternative is chosen at each *** focus on two objectives-utilitarian welfare (UTIL) and egalitarian welfare (EGAL)-and consider the computational complexity of the associated maximization problems, as well as their compatibility with strategyproofness and *** observe that maximizing UTIL is easy, but the corresponding decision problem for EGAL is NP-complete even in restricted *** complement this hardness result for EGAL with parameterized complexity analysis and an approximation ***, we show that, while a mechanism that outputs a UTIL outcome is strategyproof, all deterministic mechanisms for computing EGAL outcomes fail a very weak variant of strategyproofness, called non-obvious manipulability (NOM).However, we show that when agents have non-empty approval sets at each timestep, choosing an EGAL-maximizing outcome while breaking ties lexicographically satisfies *** proportionality, we prove that a proportional (PROP) outcome can be computed efficiently, but finding an outcome that maximizes UTIL while guaranteeing PROP is *** also derive upper and lower bounds on the price of proportionality with respect to UTIL and EGAL. © 2024 The Authors.

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