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arXiv 2024年

作者： Daub, Alexandra Mayr, Andreas Zhang, Boyao Bergherr, Elisabeth Chair of Spatial Data Science and Statistical Learning Georg-August-Universität Göttingen Platz der Göttinger Sieben 3 Göttingen37073 Germany Department of Medical Biometrics Informatics and Epidemiology University Hospital Bonn Venusberg Campus 1 Bonn53127 Germany

Component-wise gradient boosting algorithms are popular for their intrinsic variable selection and implicit regularization, which can be especially beneficial for very flexible model classes. When estimating generalized additive models for location, scale and shape (GAMLSS) by means of a component-wise gradient boosting algorithm, an important part of the estimation procedure is to determine the relative complexity of the submodels corresponding to the different distribution parameters. Existing methods either suffer from a computationally expensive tuning procedure or can be biased by structural differences in the negative gradients’ sizes, which, if encountered, lead to imbalances between the different submodels. Shrunk optimal step lengths have been suggested to replace the typical small fixed step lengths for a non-cyclical boosting algorithm limited to a Gaussian response variable in order to address this issue. In this article, we propose a new adaptive step length approach that accounts for the relative size of the fitted base-learners to ensure a natural balance between the different submodels. The new balanced boosting approach thus represents a computationally efficient and easily generalizable alternative to shrunk optimal step lengths. We implemented the balanced non-cyclical boosting algorithm for a Gaussian, a negative binomial as well as a Weibull distributed response variable and demonstrate the competitive performance of the new adaptive step length approach by means of a simulation study, in the analysis of count data modeling the number of doctor’s visits as well as for survival data in an oncological trial. © 2024, CC BY-NC-SA.

关键词： Clustering algorithms

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The phase diagram of compressed sensing with 0-norm regularization

arXiv

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arXiv 2024年

作者： Barbier, Damien Lucibello, Carlo Saglietti, Luca Krzakala, Florent Zdeborová, Lenka Ecole Polytechnique Fédérale de Lausanne Information Learning and Physics lab Statistical Physics of Computation lab LausanneCH-1015 Switzerland Institute for Data Science and Analytics Bocconi University Milan Italy Department of Computing Sciences Bocconi University Milan Italy Ecole Polytechnique Fédérale de Lausanne Statistical Physics of Computation Laboratory LausanneCH-1015 Switzerland

Noiseless compressive sensing is a two-steps setting that allows for undersampling a sparse signal and then reconstructing it without loss of information. The LASSO algorithm, based on 1 regularization, provides an efficient and robust to address this problem, but it fails in the regime of very high compression rate. Here we present two algorithms based on 0-norm regularization instead that outperform the LASSO in terms of compression rate in the Gaussian design setting for measurement matrix. These algorithms are based on the Approximate Survey Propagation, an algorithmic family within the Approximate Message Passing class. In the large system limit, they can be rigorously tracked through State Evolution equations and it is possible to exactly predict the range compression rates for which perfect signal reconstruction is possible. We also provide a statistical physics analysis of the 0-norm noiseless compressive sensing model. We show the existence of both a replica symmetric state and a 1-step replica symmmetry broken (1RSB) state for sufficiently low 0-norm regularization. The recovery limits of our algorithms are linked to the behavior of the 1RSB solution. Copyright © 2024, The Authors. All rights reserved.

关键词： Compressed sensing

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New theoretical ISM-K2 Bayesian network model for evaluating vaccination effectiveness

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Journal of Ambient Intelligence and Humanized Computing 2023年第9期14卷 12789-12805页

作者： Xie, Xiaoliang Xie, Bingqi Xiong, Dan Hou, Muzhou Zuo, Jinxia Wei, Guo Chevallier, Julien School of Mathematics and Statistics Hunan University of Technology and Busin Ess Changsha410205 China School of Mathematics and Statistics Central South University Changsha410083 China Key Laboratory of Hunan Province for Statistical Learning and Intelligent Computation Hunan University of Technology and Business Hunan Changsha410205 China Institute of Big Data and Internet Innovation Hunan University of Technology and Business Changsha410205 China Department of Mathematics and Computer Science University of North Carolina at Pembroke PembrokeNC28372 United States 184 boulevard Saint-Germain Paris75006 France 2 rue de la Liberté Saint-Denis93526 France

Aiming at the difficulty in obtaining a complete Bayesian network (BN) structure directly through search-scoring algorithms, authors attempted to incorporate expert judgment and historical data to construct an interpretive structural model with an ISM-K2 algorithm for evaluating vaccination effectiveness (VE). By analyzing the influenza vaccine data provided by Hunan Provincial Center for Disease Control and Prevention, risk factors influencing VE in each link in the process of "Transportation—Storage—Distribution—Inoculation" were systematically investigated. Subsequently, an evaluation index system of VE and an ISM-K2 BN model were developed. Findings include: (1) The comprehensive quality of the staff handling vaccines has a significant impact on VE;(2) Predictive inference and diagnostic reasoning through the ISM-K2 BN model are stable, effective, and highly interpretable, and consequently, the post-production supervision of vaccines is enhanced. The study provides a theoretical basis for evaluating VE and a scientific tool for tracking the responsibility of adverse events of ineffective vaccines, which has the value of promotion in improving VE and reducing the transmission rate of infectious diseases. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Bayesian networks

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Network-based Neighborhood Regression

arXiv

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arXiv 2024年

作者： Zhen, Yaoming Du, Jin-Hong Department of Statistical Sciences University of Toronto TorontoONM5G 1Z5 Canada Department of Statistics and Data Science Carnegie Mellon University PittsburghPA15213 United States Machine Learning Department Carnegie Mellon University PittsburghPA15213 United States

Given the ubiquity of modularity in biological systems, module-level regulation analysis is vital for understanding biological systems across various levels and their dynamics. Current statistical analysis on biological modules predominantly focuses on either detecting the functional modules in biological networks or sub-group regression on the biological features without using the network data. This paper proposes a novel network-based neighborhood regression framework whose regression functions depend on both the global community-level information and local connectivity structures among entities. An efficient community-wise least square optimization approach is developed to uncover the strength of regulation among the network modules while enabling asymptotic inference. With random graph theory, we derive non-asymptotic estimation error bounds for the proposed estimator, achieving exact minimax optimality. Unlike the root-n consistency typical in canonical linear regression, our model exhibits linear consistency in the number of nodes n, highlighting the advantage of incorporating neighborhood information. The effectiveness of the proposed framework is further supported by extensive numerical experiments. Application to whole-exome sequencing and RNA-sequencing Autism datasets demonstrates the usage of the proposed method in identifying the association between the gene modules of genetic variations and the gene modules of genomic differential expressions. Copyright © 2024, The Authors. All rights reserved.

关键词： Genes

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Theory and applications of the Sum-Of-Squares technique

arXiv

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arXiv 2023年

作者： Bach, Francis Cornacchia, Elisabetta Pesce, Luca Piccioli, Giovanni Inria Ecole Normale Supérieure PSL Research University Paris France Mathematical Data Science laboratory LausanneCH-1015 Switzerland Information Learning and Physics laboratory LausanneCH-1015 Switzerland Statistical Physics of Computation laboratory LausanneCH-1015 Switzerland

The Sum-of-Squares (SOS) approximation method is a technique used in optimization problems to derive lower bounds on the optimal value of an objective function. By representing the objective function as a sum of squares in a feature space, the SOS method transforms non-convex global optimization problems into solvable semidefinite programs. This note presents an overview of the SOS method. We start with its application in finite-dimensional feature spaces and, subsequently, we extend it to infinite-dimensional feature spaces using reproducing kernels (k-SOS). Additionally, we highlight the utilization of SOS for estimating some relevant quantities in information theory, including the log-partition function. © 2023, CC BY.

关键词： Information theory

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On the Atypical Solutions of the Symmetric Binary Perceptron

arXiv

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arXiv 2023年

作者： Barbier, Damien Alaoui, Ahmed El Krzakala, Florent Zdeborová, Lenka Information Learning & Physics laboratory École Polytechnique Fédérale de Lausanne Switzerland Department of Statistics and Data Science Cornell University IthacaNY United States Statistical Physics of Computation Laboratory École Polytechnique Fédérale de Lausanne Switzerland

We study the random binary symmetric perceptron problem, focusing on the behavior of rare high-margin solutions. While most solutions are isolated, we demonstrate that these rare solutions are part of clusters of extensive entropy, heuristically corresponding to non-trivial fixed points of an approximate message-passing algorithm. We enumerate these clusters via a local entropy, defined as a Franz-Parisi potential, which we rigorously evaluate using the first and second moment methods in the limit of a small constraint density α (corresponding to vanishing margin κ) under a certain assumption on the concentration of the entropy. This examination unveils several intriguing phenomena: i) We demonstrate that these clusters have an entropic barrier in the sense that the entropy as a function of the distance from the reference high-margin solution is non-monotone when κ ≤ 1.429 √−α/log α, while it is monotone otherwise, and that they have an energetic barrier in the sense that there are no solutions at an intermediate distance from the reference solution when κ ≤ 1.239 √−α/log α. The critical scaling of the margin κ in √−α/log α corresponds to the one obtained from the earlier work of Gamarnik et al. [62] for the overlap-gap property, a phenomenon known to present a barrier to certain efficient algorithms. ii) We establish using the replica method that the complexity (the logarithm of the number of clusters of such solutions) versus entropy (the logarithm of the number of solutions in the clusters) curves are partly non-concave and correspond to very large values of the Parisi parameter, with the equilibrium being reached when the Parisi parameter diverges. © 2023, CC BY.

关键词： Entropy

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BENCHMARKING COMMUNITY DRUG RESPONSE PREDICTION MODELS: dataSETS, MODELS, TOOLS, AND METRICS FOR CROSS-dataSET GENERALIZATION ANALYSIS

arXiv

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arXiv 2025年

作者： Partin, Alexander Vasanthakumari, Priyanka Narykov, Oleksandr Wilke, Andreas Koussa, Natasha Jones, Sara E. Zhu, Yitan Overbeek, Jamie C. Jain, Rajeev Fernando, Gayara Demini Sanchez-Villalobos, Cesar Garcia-Cardona, Cristina Mohd-Yusof, Jamaludin Chia, Nicholas Wozniak, Justin M. Ghosh, Souparno Pal, Ranadip Brettin, Thomas S. Weil, M. Ryan Stevens, Rick L. Division of Data Science and Learning Argonne National Laboratory LemontIL United States Frederick National Laboratory for Cancer Research Cancer Data Science Initiatives Cancer Research Technology Program FrederickMD United States Department of Statistics University of Nebraska–Lincoln LincolnNE United States Department of Electrical & Computer Engineering Texas Tech University LubbockTX United States Division of Computer Computational and Statistical Sciences Los Alamos National Laboratory Los AlamosNM United States Department of Computer Science The University of Chicago ChicagoIL United States

Deep learning (DL) and machine learning (ML) models have shown promise in drug response prediction (DRP), yet their ability to generalize across datasets remains an open question, raising concerns about their real-world applicability. Due to the lack of standardized benchmarking approaches, model evaluations and comparisons often rely on inconsistent datasets and evaluation criteria, making it difficult to assess true predictive capabilities. In this work, we introduce a benchmarking framework for evaluating cross-dataset prediction generalization in DRP models. Our framework incorporates five publicly available drug screening datasets, six standardized DRP models, and a scalable workflow for systematic evaluation. To assess model generalization, we introduce a set of evaluation metrics that quantify both absolute performance (e.g., predictive accuracy across datasets) and relative performance (e.g., performance drop compared to within-dataset results), enabling a more comprehensive assessment of model transferability. Our results reveal substantial performance drops when models are tested on unseen datasets, underscoring the importance of rigorous generalization assessments. While several models demonstrate relatively strong cross-dataset generalization, no single model consistently outperforms across all datasets. Furthermore, we identify CTRPv2 as the most effective source dataset for training, yielding higher generalization scores across target datasets. By sharing this standardized evaluation framework with the community, our study aims to establish a rigorous foundation for model comparison, and accelerate the development of robust DRP models for real-world applications. © 2025, CC BY.

关键词： Prediction models

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Structural phase transitions between layered Indium Selenide for integrated photonic memory

arXiv

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arXiv 2025年

作者： Li, Tiantian Wang, Yong Li, Wei Mao, Dun Benmore, Chris J. Evangelista, Igor Xing, Huadan Li, Qiu Wang, Feifan Sivaraman, Ganesh Janotti, Anderson Law, Stephanie Gu, Tingyi Department of Electrical and Computer Engineering University of Delaware NewarkDE19716 United States Department of Materials Science and Engineering University of Delaware NewarkDE19716 United States Los Alamos National Laboratory Computer Computational and Statistical Sciences Division Los AlamosNM87545 United States X-ray Science Division Argonne National Laboratory LemontIL60439 United States Data Science and Learning Division Argonne National Laboratory LemontIL60439 United States

The primary mechanism of optical memoristive devices relies on the phase transitions between amorphous-crystalline states. The slow or energy hungry amorphous-crystalline transitions in optical phase-change materials are detrimental to the devices’ scalability and performance. Leveraging the integrated photonic platform, we demonstrate a single nanosecond pulse triggered nonvolatile and reversible switching between two layered structures of indium selenide (In2Se3). High resolution pair distribution function reveals the detailed atomistic transition pathways between the layered structures. With inter-layer ‘shear glide’ and isosymmetric phase transition, the switching between α- and β-structural states contain low re-configurational entropy, allowing reversible switching between layered structures. Broadband refractive index contrast, optical transparency, and volumetric effect in the crystalline-crystalline phase transition are experimentally characterized in molecular beam epitaxy-grown thin films and compared to ab-initial calculations. The nonlinear resonator transmission spectra measure incremental linear loss rate of 3.3 GHz introduced by 1.5 µm long In2Se3 covered layer, resulting from the combinations of material absorption and scattering. © 2025, CC BY.

关键词： Molecular beam epitaxy

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"Spatial Joint Models through Bayesian Structured Piece-wise Additive Joint Modelling for Longitudinal and Time-to-Event data"

arXiv

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arXiv 2023年

作者： Rappl, Anja Kneib, Thomas Lang, Stefan Bergherr, Elisabeth Institute of Medical Informatics Biometry and Epidemiology Friedrich-Alexander Universität Erlangen-Nürnberg Germany Chair of Statistics Georg-August-Universität Göttingen Germany Department of Statistics Universität Innsbruck Austria Chair of Spatial Data Science and Statistical Learning Georg-August-Universität Göttingen Germany

Joint models for longitudinal and time-to-event data have seen many developments in recent years. Though spatial joint models are still rare and the traditional proportional hazards formulation of the time-to-event part of the model is accompanied by computational challenges. We propose a joint model with a piece-wise exponential formulation of the hazard using the counting process representation of a hazard and structured additive predictors able to estimate (non-)linear, spatial and random effects. Its capabilities are assessed in a simulation study comparing our approach to an established one and highlighted by an example on physical functioning after cardiovascular events from the German Ageing Survey. The Structured Piece-wise Additive Joint Model yielded good estimation performance, also and especially in spatial effects, while being double as fast as the chosen benchmark approach and performing stable in imbalanced data setting with few events. © 2023, CC BY.

关键词： Additives

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Benchmarking Visual-Inertial Deep Multimodal Fusion for Relative Pose Regression and Odometry-aided Absolute Pose Regression

arXiv

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arXiv 2022年

作者： Ott, Felix Raichur, Nisha Lakshmana Rügamer, David Feigl, Tobias Neumann, Heiko Bischl, Bernd Mutschler, Christopher The Fraunhofer IIS Fraunhofer Institute for Integrated Circuits IIS Nuremberg Germany The Statistical Learning and Data Science LMU Munich Munich Germany Munich Germany The Technical University of Dortmund Germany The Institute of Neural Information Processing Ulm University Ulm Germany

Visual-inertial localization is a key problem in computer vision and robotics applications such as virtual reality, self-driving cars, and aerial vehicles. The goal is to estimate an accurate pose of an object when either the environment or the dynamics are known. Absolute pose regression (APR) techniques directly regress the absolute pose from an image input in a known scene using convolutional and spatio-temporal networks. Odometry methods perform relative pose regression (RPR) that predicts the relative pose from a known object dynamic (visual or inertial inputs). The localization task can be improved by retrieving information from both data sources for a cross-modal setup, which is a challenging problem due to contradictory tasks. In this work, we conduct a benchmark to evaluate deep multimodal fusion based on pose graph optimization and attention networks. Auxiliary and Bayesian learning are utilized for the APR task. We show accuracy improvements for the APR-RPR task and for the RPR-RPR task for aerial vehicles and hand-held devices. We conduct experiments on the EuRoC MAV and PennCOSYVIO datasets and record and evaluate a novel industry *** Codes 68T40, 65D19 Copyright © 2022, The Authors. All rights reserved.

关键词： Bayesian networks

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