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Joint models in big data: simulation-based guidelines for required data quality in longitudinal electronic health records

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BioData Mining 2025年第1期18卷 1-27页

作者： Hunsdieck, Berit Bender, Christian Ickstadt, Katja Mielke, Johanna Computational Biology Bayer AG Wuppertal Germany Department of Statistics TU Dortmund University Dortmund Germany Lamarr-Institute for Machine Learning and Artificial Intelligence Dortmund Germany

Background: Over the past decade an increase in usage of electronic health data (EHR) by office-based physicians and hospitals has been reported. However, these data types come with challenge regarding completeness and data quality and it is, especially for more complex models, unclear how these characteristics influence the performance. Methods: In this paper, we focus on joint models which combines longitudinal modelling with survival modelling to incorporate all available information. The aim of this paper is to establish simulation-based guidelines for the necessary quality of longitudinal EHR data so that joint models perform better than cox models. We conducted an extensive simulation study by systematically and transparently varying different characteristics of data quality, e.g., measurement frequency, noise, and heterogeneity between patients. We apply the joint models and evaluate their performance relative to traditional Cox survival modelling techniques. Results: Key findings suggest that biomarker changes before disease onset must be consistent within similar patient groups. With increasing noise and a higher measurement density, the joint model surpasses the traditional Cox regression model in terms of model performance. We illustrate the usefulness and limitations of the guidelines with two real-world examples, namely the influence of serum bilirubin on primary biliary liver cirrhosis and the influence of the estimated glomerular filtration rate on chronic kidney disease. © The Author(s) 2025.

关键词： Chronic kidney disease Joint modelling Longitudinal data application Primary care data Simulation study

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Generalization of hamiltonian algorithms 24

Generalization of hamiltonian algorithms

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Andreas Maurer Computational Statistics and Machine Learning Istituto Italiano di Tecnologia Genoa Italy

ISBN: (纸本)9798331314385

The paper proves generalization results for a class of stochastic learning algorithms. The method applies whenever the algorithm generates an absolutely continuous distribution relative to some a-priori measure and the Radon Nikodym derivative has subgaussian concentration. Applications are bounds for the Gibbs algorithm and randomizations of stable deterministic algorithms as well as PAC-Bayesian bounds with data-dependent priors.

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Bilevel optimization with a lower-level contraction: optimal sample complexity without warm-start

The Journal of Machine Learning Research

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The Journal of machine learning Research 2023年第1期24卷 7995-8031页

作者： Riccardo Grazzi Massimiliano Pontil Saverio Salzo Computational Statistics and Machine Learning Istituto Italiano di Tecnologia Genoa Italy and University College of London UK Universitá la Sapienza di Roma Italy and Computational Statistics and Machine Learning Istituto Italiano di Tecnologia Genoa Italy

We analyse a general class of bilevel problems, in which the upper-level problem consists in the minimization of a smooth objective function and the lower-level problem is to find the fixed point of a smooth contraction map. This type of problems include instances of meta-learning, equilibrium models, hyperparameter optimization and data poisoning adversarial attacks. Several recent works have proposed algorithms which warm-start the lower-level problem, i.e. they use the previous lower-level approximate solution as a staring point for the lower-level solver. This warm-start procedure allows one to improve the sample complexity in both the stochastic and deterministic settings, achieving in some cases the order-wise optimal sample complexity. However, there are situations, e.g., meta learning and equilibrium models, in which the warm-start procedure is not well-suited or ineffective. In this work we show that without warm-start, it is still possible to achieve order-wise (near) optimal sample complexity. In particular, we propose a simple method which uses (stochastic) fixed point iterations at the lower-level and projected inexact gradient descent at the upper-level, that reaches an ε-stationary point using O(ε-2) and Õ(ε-1) samples for the stochastic and the deterministic setting, respectively. Finally, compared to methods using warm-start, our approach yields a simpler analysis that does not need to study the coupled interactions between the upper-level and lower-level iterates.

关键词： bilevel optimization warm-start non-convex optimization implicit differentiation hypergradient sample complexity

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Active multiple testing with proxy p-values and e-values

arXiv

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arXiv 2025年

作者： Xu, Ziyu Wang, Catherine Wasserman, Larry Roeder, Kathryn Ramdas, Aaditya Department of Statistics and Data Science United States Machine Learning Department Germany Computational Biology Department Carnegie Mellon University United States

Researchers often lack the resources to test every hypothesis of interest directly or compute test statistics comprehensively, but often possess auxiliary data from which we can compute an estimate of the experimental outcome. We introduce a novel approach for selecting which hypotheses to query a statistic (i.e., run an experiment, perform expensive computation, etc.) in a hypothesis testing setup by leveraging estimates (e.g., from experts, machine learning models, previous experiments, etc.) to compute proxy statistics. Our framework allows a scientist to propose a a proxy statistic, and then query the true statistic with some probability based on the value of the proxy. We make no assumptions about how the proxy is derived and it can be arbitrarily dependent with the true statistic. If the true statistic is not queried, the proxy is used in its place. We characterize "active" methods that produce valid p-values and e-values in this setting and utilize this framework in the multiple testing setting to create procedures with false discovery rate (FDR) control. Through simulations and real data analysis of causal effects in scCRISPR screen experiments, we empirically demonstrate that our proxy framework has both high power and low resource usage when our proxies are accurate estimates of the respective true statistics. Copyright © 2025, The Authors. All rights reserved.

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Hierarchical Bayesian Modelling of a Family of FRFs 14

Hierarchical Bayesian Modelling of a Family of FRFs

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14th International Workshop on Structural Health Monitoring: Designing SHM for Sustainability, Maintainability, and Reliability, IWSHM 2023

作者： Dardeno, Tina A. Bull, Lawrence A. Mills, Robin S. Dervilis, Nikolaos Worden, Keith Dynamics Research Group Department of Mechanical Engineering University of Sheffield S1 3JD United Kingdom Computational Statistics and Machine Learning Group Engineering Department University of Cambridge CB2 1PZ United Kingdom

ISBN: (纸本)9781605956930

Population-based structural health monitoring (PBSHM) aims to share valuable information among members of a population, such as normal- and damage-condition data, to improve inferences regarding the health states of the members. Even when the population is comprised of nominally-identical structures, benign variations among the members will exist as a result of slight differences in material properties, geometry, boundary conditions, or environmental effects (e.g., temperature changes). These discrepancies can affect modal properties and present as changes in the characteristics of the resonance peaks of the frequency response function (FRF). The hierarchical Bayesian approach provides a useful modelling structure for PBSHM, as population- and domain-level distributions are learnt simultaneously to bolster statistical strength among the parameters, and reduce variance among the parameter estimates. This paper provides an overview of current work, where hierarchical Bayesian models are developed for a small population of nominally-identical helicopter blades, using FRF data. These models account for benign variations that present as differences in the underlying dynamics across the input space, while also considering (and utilising) the similarities among the blades. © 2023 by DEStech Publi cations, Inc. All rights reserved

关键词： Structural health monitoring

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Tensor parametric Hamiltonian operator inference

arXiv

引用

arXiv 2025年

作者： Vijaywargiya, Arjun McQuarrie, Shane A. Gruber, Anthony Department of Applied and Computational Mathematics and Statistics University of Notre Dame United States Computational Mathematics Center for Computing Research Sandia National Laboratories Scientific Machine Learning Center for Computing Research Sandia National Laboratories

This work presents a tensor-based approach to constructing data-driven reduced-order models corresponding to semi-discrete partial differential equations with canonical Hamiltonian structure. By expressing parameter-varying operators with affine dependence as contractions of a generalized parameter vector against a constant tensor, this method leverages the operator inference framework to capture parametric dependence in the learned reduced-order model via the solution to a convex, least-squares optimization problem. This leads to a concise and straightforward implementation which compactifies previous parametric operator inference approaches and directly extends to learning parametric operators with symmetry constraints—a key feature required for constructing structure-preserving surrogates of Hamiltonian systems. The proposed approach is demonstrated on both a (non-Hamiltonian) heat equation with variable diffusion coefficient as well as a Hamiltonian wave equation with variable wave *** Codes 15A69, 35B30, 65P10 © 2025, CC BY.

关键词： Tensors

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Value of Information Analysis for rationalising information gathering in building energy analysis 18

Value of Information Analysis for rationalising information ...

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18th IBPSA Conference on Building Simulation, BS 2023

作者： Langtry, Max Zhuang, Chaoqun Ward, Rebecca Makasis, Nikolas Kreitmair, Monika J. Conti, Zack Xuereb Francesco, Domenic Di Choudhary, Ruchi Energy Efficient Cities Initiative Cambridge University Engineering Department Cambridge United Kingdom Data-centric Engineering The Alan Turing Institute British Library London United Kingdom Computational Statistics & Machine Learning Group Cambridge University Engineering Department Cambridge United Kingdom

The use of monitored data to improve the accuracy of building energy models and operation of energy systems is ubiquitous, with topics such as building monitoring and Digital Twinning attracting substantial research attention. However, little attention has been paid to quantifying the value of the data collected against its cost. This paper argues that without a principled method for determining the value of data, its collection cannot be prioritised. It demonstrates the use of Value of Information analysis (VoI), which is a Bayesian Decision Analysis framework, to provide such a methodology for quantifying the value of data collection in the context of building energy modelling and analysis. Three energy decision-making examples are presented: ventilation scheduling, heat pump maintenance scheduling, and ground source heat pump design. These examples illustrate the use of VoI to support decision-making on data collection. © 2023 *** rights reserved.

关键词： Decision making

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Operator World Models for Reinforcement learning

arXiv

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arXiv 2024年

作者： Novelli, Pietro Pratticò, Marco Pontil, Massimiliano Ciliberto, Carlo Computational Statistics and Machine Learning - Istituto Italiano di Tecnologia Genova16100 Italy AI Centre Computer Science Department University College London London United Kingdom

Policy Mirror Descent (PMD) is a powerful and theoretically sound methodology for sequential decision-making. However, it is not directly applicable to Reinforcement learning (RL) due to the inaccessibility of explicit action-value functions. We address this challenge by introducing a novel approach based on learning a world model of the environment using conditional mean embeddings. Leveraging tools from operator theory we derive a closed-form expression of the action-value function in terms of the world model via simple matrix operations. Combining these estimators with PMD leads to POWR, a new RL algorithm for which we prove convergence rates to the global optimum. Preliminary experiments in finite and infinite state settings support the effectiveness of our method. Copyright © 2024, The Authors. All rights reserved.

关键词： Reinforcement learning

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Bilevel Optimization with a Lower-level Contraction: Optimal Sample Complexity without Warm-start

arXiv

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arXiv 2022年

作者： Grazzi, Riccardo Pontil, Massimiliano Salzo, Saverio Computational Statistics and Machine Learning Istituto Italiano di Tecnologia Genoa Italy University College of London United Kingdom Universitá la Sapienza di Roma Italy and Computational Statistics and Machine Learning Istituto Italiano di Tecnologia Genoa Italy

We analyse a general class of bilevel problems, in which the upper-level problem consists in the minimization of a smooth objective function and the lower-level problem is to find the fixed point of a smooth contraction map. This type of problems include instances of meta-learning, equilibrium models, hyperparameter optimization and data poisoning adversarial attacks. Several recent works have proposed algorithms which warm-start the lower-level problem, i.e. they use the previous lower-level approximate solution as a staring point for the lower-level solver. This warm-start procedure allows one to improve the sample complexity in both the stochastic and deterministic settings, achieving in some cases the order-wise optimal sample complexity. However, there are situations, e.g., meta learning and equilibrium models, in which the warm-start procedure is not well-suited or ineffective. In this work we show that without warm-start, it is still possible to achieve order-wise (near) optimal sample complexity. In particular, we propose a simple method which uses (stochastic) fixed point iterations at the lower-level and projected inexact gradient descent at the upper-level, that reaches an ϵ-stationary point using O(ϵ−2) and Õ(ϵ−1) samples for the stochastic and the deterministic setting, respectively. Finally, compared to methods using warm-start, our approach yields a simpler analysis that does not need to study the coupled interactions between the upper-level and lower-level iterates. Copyright © 2022, The Authors. All rights reserved.

关键词： Convex optimization

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Anomaly Detection in Offshore Wind Turbine Structures using Hierarchical Bayesian Modelling 14

Anomaly Detection in Offshore Wind Turbine Structures using ...

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14th International Workshop on Structural Health Monitoring: Designing SHM for Sustainability, Maintainability, and Reliability, IWSHM 2023

作者： Smith, Simon M. Hughes, Aidan J. Dardeno, Tina A. Bull, Lawrence A. Dervilis, Nikolaos Worden, Keith Dynamics Research Group Department of Mechanical Engineering The 2University of Sheffield Western Bank SheffieldS10 2TN United Kingdom Computational Statistics and Machine Learning Group Department of Engineering University of Cam- bridge CambridgeCB3 0FA United Kingdom

ISBN: (纸本)9781605956930

Population-based structural health monitoring (PBSHM), aims to share information between members of a population. An offshore wind (OW) farm could be considered as a population of nominally-identical wind-turbine structures. However, benign variations exist among members, such as geometry, sea-bed conditions and temperature differences. These factors could influence structural properties and therefore the dynamic response, making it more difficult to detect structural problems via traditional SHM techniques. This paper explores the use of a hierarchical Bayesian model to infer expected soil stiffness distributions at both population and local levels, as a basis to perform anomaly detection, in the form of scour, for new and existing turbines. To do this, observations of natural frequency will be generated as though they are from a small population of wind turbines. Differences between individual observations will be introduced by postulating distributions over the soil stiffness and measurement noise, as well as reducing soil depth (to represent scour), in the case of anomaly detection. © 2023 by DEStech Publi cations, Inc. All rights reserved

关键词： Anomaly detection

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