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Assessing treatment efficacy for interval-censored endpoints using multistate semi-Markov models fit to multiple data streams

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arXiv 2025年

作者： Morsomme, Raphaël Liang, C. Jason Mateja, Allyson Follmann, Dean A. O’Brien, Meagan P. Wang, Chenguang Fintzi, Jonathan Biostatistics Research Branch National Institute of Allergy and Infectious Diseases National Institutes of Health BethesdaMD United States Clinical Monitoring Research Program Directorate Frederick National Laboratory for Cancer Research FrederickMD United States Regeneron Pharmaceuticles TarrytownNY United States

We introduce a computationally efficient and general approach for utilizing multiple, possibly interval-censored, data streams to study complex biomedical endpoints using multistate semi-Markov models. Our motivating application is the REGEN-2069 trial, which investigated the protective efficacy (PE) of the monoclonal antibody combination REGEN-COV against SARS-CoV-2 when administered prophylactically to individuals in households at high risk of secondary transmission. Using data on symptom onset, episodic RT-qPCR sampling, and serological testing, we estimate the PE of REGEN-COV for asymptomatic infection, its effect on seroconversion following infection, and the duration of viral shedding. We find that REGEN-COV reduced the risk of asymptomatic infection and the duration of viral shedding, and led to lower rates of seroconversion among asymptomatically infected participants. Our algorithm for fitting semi-Markov models to interval-censored data employs a Monte Carlo expectation maximization (MCEM) algorithm combined with importance sampling to efficiently address the intractability of the marginal likelihood when data are intermittently observed. Our algorithm provide substantial computational improvements over existing methods and allows us to fit semi-parametric models despite complex coarsening of the data. © 2025, CC BY.

关键词： expectation maximization algorithm

Optimising expectation with guarantees for window mean payoff in Markov decision processes

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arXiv 2025年

作者： Gaba, Pranshu Guha, Shibashis Tata Institute of Fundamental Research Mumbai India

The window mean-payoff objective strengthens the classical mean-payoff objective by computing the mean-payoff over a finite window that slides along an infinite path. Two variants have been considered: in one variant, the maximum window length is fixed and given, while in the other, it is not fixed but is required to be bounded. In this paper, we look at the problem of synthesising strategies in Markov decision processes that maximise the window mean-payoff value in expectation, while also simultaneously guaranteeing that the value is above a certain threshold. We solve the synthesis problem for three different kinds of guarantees: sure (that needs to be satisfied in the worst-case, that is, for an adversarial environment), almost-sure (that needs to be satisfied with probability one), and probabilistic (that needs to be satisfied with at least some given probability p). We show that for fixed window mean-payoff objective, all the three problems are in PTIME, while for bounded window mean-payoff objective, they are in NP ∩ coNP, and thus have the same complexity as for maximising the expected performance without any guarantee. Moreover, we show that pure finite-memory strategies suffice for maximising the expectation with sure and almost-sure guarantees, whereas, for maximising expectation with a probabilistic guarantee, randomised strategies are necessary in general. © 2025, CC BY.

关键词： expectation maximization algorithm

Quantization Of Probability Measures In Maximum Mean Discrepancy Distance

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arXiv 2025年

作者： Mehraban, Zahra Pichler, Alois Faculty of Mathematics University of Technology ChemnitzD-09111 Germany

Accurate approximation of probability measures is essential in numerical applications. This paper explores the quantization of probability measures using the maximum mean discrepancy (MMD) distance as a guiding metric. We first investigate optimal approximations by determining the best weights, followed by addressing the problem of optimal facility locations. To facilitate efficient computation, we reformulate the nonlinear objective as expectations over a product space, enabling the use of stochastic approximation methods. For the Gaussian kernel, we derive closed-form expressions to develop a deterministic optimization approach. By integrating stochastic approximation with deterministic techniques, our framework achieves precise and efficient quantization of continuous distributions, with significant implications for machine learning and signal processing applications. © 2025, CC BY.

关键词： expectation maximization algorithm

Reasoning-Enhanced Self-Training for Long-Form Personalized Text Generation

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arXiv 2025年

作者： Salemi, Alireza Li, Cheng Zhang, Mingyang Mei, Qiaozhu Kong, Weize Chen, Tao Li, Zhuowan Bendersky, Michael Zamani, Hamed University of Massachusetts Amherst United States Google DeepMind United Kingdom University of Michigan United States

Personalized text generation requires a unique ability of large language models (LLMs) to learn from context that they often do not encounter during their standard training. One way to encourage LLMs to better use personalized context for generating outputs that better align with the user's expectations is to instruct them to reason over the user's past preferences, background knowledge, or writing style. To achieve this, we propose Reasoning-Enhanced Self-Training for Personalized Text Generation (REST-PG), a framework that trains LLMs to reason over personal data during response generation. REST-PG first generates reasoning paths to train the LLM's reasoning abilities and then employs expectation-maximization Reinforced Self-Training to iteratively train the LLM based on its own high-reward outputs. We evaluate REST-PG on the LongLaMP benchmark, consisting of four diverse personalized long-form text generation tasks. Our experiments demonstrate that REST-PG achieves significant improvements over state-of-the-art baselines, with an average relative performance gain of 14.5% on the benchmark. Copyright © 2025, The Authors. All rights reserved.

关键词： expectation maximization algorithm

Allocating Variance to Maximize expectation

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arXiv 2025年

作者： Leme, Renato Purita Paes Stein, Cliff Teng, Yifeng Worah, Pratik Google Inc United States Google Inc. Columbia University United States

We design efficient approximation algorithms for maximizing the expectation of the supremum of families of Gaussian random variables. In particular, let OPT:= maxσ1,···,σn E ∑mj=1 maxi∈Sj Xi, where Xi are Gaussian, Sj ⊂ [n] and ∑ i σi2 = 1, then our theoretical results include: • We characterize the optimal variance allocation – it concentrates on a small subset of variables as |Sj| increases, • A polynomial time approximation scheme (PTAS) for computing OPT when m = 1, and • An O(log n) approximation algorithm for computing OPT for general m > 1. Such expectation maximization problems occur in diverse applications, ranging from utility maximization in auctions markets to learning mixture models in quantitative genetics. Copyright © 2025, The Authors. All rights reserved.

关键词： expectation maximization algorithm

Design and analysis of direction of arrival using hybrid expectation-maximization and MUSIC for wireless communication

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OPTIK 2018年 170卷 48-55页

作者： Malla, Prince Priya Mukherjee, Amrit Routray, Sidheswar Palai, G. KL Univ Dept Elect & Commun Engn Guntur Andhra Prades India KIIT Univ Sch Elect Engn Bhubaneswar Odisha India GITA Dept Elect & Commun Engn Bhubaneswar Odisha India

As multiple signal classification (MUSIC) algorithm is unable to estimate the direction of arrival (DOA) of highly correlated or coherent signal, based on array processing and decomposition of covariance matrix of incident signals due to its lack of source identification. In the proposed hybrid model, we have considered expectation maximization (EM) algorithm for precise identification of the DOA of highly correlated signals in wireless communication applications. The proposed model is analysed, simulated and verified using two different source signals. The mathematical analysis shows substantial closeness with the simulated results. The robustness of the proposed algorithm is further verified by adding noise to the incident signals. The utilization of the EM algorithm in the proposed hybrid approach reduces the time requirement and mathematical complexity of MUSIC algorithm for DOA estimation. By exploiting the EM algorithm, the original transmitted signal and its arriving angle on the antenna element are estimated from the mixture of interferer's signal, transmitted signal, multipath component and noise. Hence it is straightforward to recognise the desired signal.

关键词： Direction of arrival Music algorithm expectation maximization algorithm Wireless communication

Probabilistic Formulations for System Identification of Linear Dynamics with Bilinear Observation Models

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arXiv 2025年

作者： Liu, Diyou Khosravi, Mohammad Delft Center for Systems and Control Delft University of Technology Delft Netherlands

In this paper, we address the identification problem for the systems characterized by linear time-invariant dynamics with bilinear observation models. More precisely, we consider a suitable parametric description of the system and formulate the identification problem as the estimation of the parameters defining the mathematical model of the system using the observed input-output data. To this end, we propose two probabilistic frameworks. The first framework employs the Maximum Likelihood (ML) approach, which accurately finds the optimal parameter estimates by maximizing a likelihood function. Subsequently, we develop a tractable first-order method to solve the optimization problem corresponding to the proposed ML approach. Additionally, to further improve tractability and computational efficiency of the estimation of the parameters, we introduce an alternative framework based on the expectation–maximization (EM) approach, which estimates the parameters using an appropriately designed cost function. We show that the EM cost function is invex, which ensures the existence and uniqueness of the optimal solution. Furthermore, we derive the closed-form solution for the optimal parameters and also prove the recursive feasibility of the EM procedure. Through extensive numerical experiments, the practical implementation of the proposed approaches is demonstrated, and their estimation efficacy is verified and compared, highlighting the effectiveness of the methods to accurately estimate the system parameters and their potential for real-world applications in scenarios involving bilinear observation structures. © 2025, CC BY.

关键词： expectation maximization algorithm

Priority-Based Weighted Constrained Crowd Judgement Analysis with Quantum Genetic algorithm 4th

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Priority-Based Weighted Constrained Crowd Judgement Analysis...

4th International Conference on Advanced Network Technologies and Intelligent Computing, ANTIC 2024

作者： Mandal, Suraj Chatterjee, Sujoy Mukhopadhyay, Anirban University of Kalyani Kalyani India University of Campinas Sao Paulo Brazil Indian Institute of Technology Kanpur Kanpur India

ISBN: (纸本)9783031837920

Crowdsourcing has already obtained a lot of attention from researchers due to the enormous power of solving complex problems in less time and at a minimal cost. Most of the research considers finding aggregated judgment from multiple diverse opinions of workers, and several algorithms such as majority-voting, weighted majority voting, and expectation-maximization (EM) algorithms are widely accepted. Recently, a relatively unexplored area of judgement analysis algorithm was introduced in the literature, and each crowd worker provides their opinions that comprise multiple components for a single question, and there is a constraint for each pair of components. However, in state-of-the-art approaches, the weight of the opinions is not considered. In this work, a new variant of the problem is introduced, while the weights between the components are considered. More importantly, the weights are not predefined, rather they are collected from the crowd workers. Thus, it can help us to explore many other solutions based on the users’ preferences which is very difficult to obtain from decision-makers if the crowdsourcing technique is not employed. Quantum computing has been a new approach to computer science that explores the idea of parallelism in computing. In this work, we have introduced a quantum genetic algorithm that explores solving the multiple objective-based weighted constrained crowd judgment analysis. In addition to that, we have introduced a new variant of constrained crowd judgment analysis that not only explores the solution to the multiple opinion-based solutions but also makes a better interpretation of the solutions by including textual information and additional weight parameters. The algorithm is applied to a real-life crowdsourced dataset obtained from the campus of the University of Kalyani. The experimental results demonstrate the applicability of the proposed method while providing a better solution compared to the traditional method. © The Author(s),

关键词： expectation maximization algorithm

Cluster weighted models for functional data

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arXiv 2025年

作者： Anton, Cristina Smith, Iain Department of Mathematics and Statistics MacEwan University 10700 – 104 Avenue EdmontonABT5J 4S2 Canada

We propose a method, funWeightClust, based on a family of parsimonious models for clustering heterogeneous functional linear regression data. These models extend cluster weighted models to functional data, and they allow for multivariate functional responses and predictors. The proposed methodology follows the approach used by the the functional high dimensional data clustering (funHDDC) method. We construct an expectation maximization (EM) algorithm for parameter estimation. Using simulated and benchmark data we show that funWeightClust outperforms funHDDC and several two-steps clustering methods. We also use funWeightClust to analyze traffic patterns in Edmonton, Canada. © 2025, CC BY-NC-ND.

关键词： expectation maximization algorithm