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Inference With Aggregate Data in probabilistic graphical models: An Optimal Transport Approach

IEEE TRANSACTIONS ON AUTOMATIC CONTROL 2022年第9期67卷 4483-4497页

作者： Singh, Rahul Haasler, Isabel Zhang, Qinsheng Karlsson, Johan Chen, Yongxin Georgia Inst Technol Sch Aerosp Engn Atlanta GA 30332 USA KTH Royal Inst Technol Dept Math Div Optimizat & Syst Theory S-10044 Stockholm Sweden

We consider inference (filtering) problems over probabilistic graphical models with aggregate data generated by a large population of individuals. We propose a new efficient belief propagation type algorithm over tree graphs with polynomial computational complexity as well as a global convergence guarantee. This is in contrast to previous methods that either exhibit prohibitive complexity as the population grows or do not guarantee convergence. Our method is based on optimal transport, or more specifically, multimarginal optimal transport theory. In particular, we consider an inference problem with aggregate observations, that can be seen as a structured multimarginal optimal transport problem where the cost function decomposes according to the underlying graph. Consequently, the celebrated Sinkhorn/iterative scaling algorithm for multi-marginal optimal transport can be leveraged together with the standard belief propagation algorithm to establish an efficient inference scheme which we call Sinkhorn belief propagation (SBP). We further specialize the SBP algorithm to cases associated with hidden Markov models due to their significance in control and estimation. We demonstrate the performance of our algorithm on applications such as inferring population flow from aggregate observations. We also show that in the special case where the aggregate observations are in Dirac form, our algorithm naturally reduces to the standard belief propagation algorithm.

关键词： Inference algorithms Aggregates Hidden Markov models Signal processing algorithms Filtering graphical models Data aggregation Filtering hidden markov models optimal transport optimization probabilistic graphical models

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Applicability of probabilistic graphical models for early detection of SARS-CoV-2 reactive antibodies after SARS-CoV-2 vaccination in hematological patients

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ANNALS OF HEMATOLOGY 2022年第9期101卷 2053-2067页

作者： Luis Pinana, Jose Rodriguez-Belenguer, Pablo Caballero, Dolores Martino, Rodrigo Lopez-Corral, Lucia Terol, Maria-Jose Vazquez, Lourdes Calabuig, Marisa Sanz-Linares, Gabriela Marin-Jimenez, Francisca Alonso, Carmen Montoro, Juan Ferrer, Elena Facal, Ana Pascual, Maria-Jesus Rodriguez-Fernandez, Alicia Olave, Maria T. Cascales-Hernandez, Almudena Gago, Beatriz Hernandez-Rivas, Jose-Angel Villalon, Lucia Corona, Magdalena Roldan-Perez, Alicia Ribes-Amoros, Julia Gonzalez-Santillana, Clara Garcia-Sanz, Ramon Navarro, David Serrano-Lopez, Antonio J. Cedillo, Angel Soria-Olivas, Emilio Sureda, Anna Solano, Carlos Hosp Clin Univ Valencia Hematol Dept Avda Blasco Ibanez 17 Valencia 46010 Spain Hosp Clin Univ Valencia Fdn INCLIVA Inst Invest Sanitaria Valencia Spain Univ Pompeu Fabra Hosp del Mar Med Res Inst IMIM Dept Expt & Hlth Sci Res Program Biomed Informat GRIB Barcelona Spain Univ Hosp Salamanca Ctr Invest Canc IBMCC USAL CSIC Hematol Dept IBSALCIBERONC Salamanca Spain Hosp Santa Creu & Sant Pau Hematol Div Barcelona Spain Univ Barcelona Inst Catala Oncol Hosp Clin Hematol Div IDIBELL Barcelona Spain Hosp Gen Univ Elx Hematol Div Elche Spain Hosp Arnau Vilanova Hematol Div Valencia Spain Hosp Univ & Politecn La Fe Hematol Div Valencia Spain Hosp Reg Univ Carlos Haya Hematol Div Malaga Spain Hosp Univ Virgen Macarena Hematol Div Seville Spain Hosp Clin Univ Lozano Blesa Hematol Div IIS Aragon Zaragoza Spain Hosp Clin Univ Virgen de la Arrixaca Hematol Div Murcia Spain Hosp Univ Infanta Leonor Hematol Div Madrid Spain Hosp Univ Fdn Alcorcon Hematol Div Madrid Spain Hosp Ramon & Cajal Hematol Div Madrid Spain Hosp Univ Infanta Sofia Hematol Div Madrid Spain Hosp Univ Fuenlabrada Hematol Div Madrid Spain Univ Valencia Sch Med Dept Med Valencia Spain Hosp Clin Univ Valencia Microbiol Dept Valencia Spain Univ Valencia Intelligent Data Anal Lab ETSE IDAL Valencia Spain Hematopoiet Stem Cell Transplantat & Cell Therapy Valencia Spain

Prior studies of antibody response after full SARS-CoV-2 vaccination in hematological patients have confirmed lower antibody levels compared to the general population. Serological response in hematological patients varies widely according to the disease type and its status, and the treatment given and its timing with respect to vaccination. Through probabilistic machine learning graphical models, we estimated the conditional probabilities of having detectable anti-SARS-CoV-2 antibodies at 3-6 weeks after SARS-CoV-2 vaccination in a large cohort of patients with several hematological diseases (n= 1166). Most patients received mRNA-based vaccines (97%), mainly Moderna (R) mRNA-1273 (74%) followed by Pfizer-BioNTech (R) BNT162b2 (23%). The overall antibody detection rate at 3 to 6 weeks after full vaccination for the entire cohort was 79%. Variables such as type of disease, timing of anti-CD20 monoclonal antibody therapy, age, corticosteroids therapy, vaccine type, disease status, or prior infection with SARS-CoV-2 are among the most relevant conditions influencing SARS-CoV-2-IgG-reactive antibody detection. A lower probability of having detectable antibodies was observed in patients with B-cell non-Hodgkin's lymphoma treated with anti-CD20 monoclonal antibodies within 6 months before vaccination (29.32%), whereas the highest probability was observed in younger patients with chronic myeloproliferative neoplasms (99.53%). The Moderna (R) mRNA-1273 compound provided higher probabilities of antibody detection in all scenarios. This study depicts conditional probabilities of having detectable antibodies in the whole cohort and in specific scenarios such as B cell NHL, CLL, MM, and cMPN that may impact humoral responses. These results could be useful to focus on additional preventive and/or monitoring interventions in these highly immunosuppressed hematological patients.

关键词： probabilistic graphical models Bayesian Networks mRNA vaccine SARS-CoV-2 vaccines Hematological malignancies Non-Hodgkin lymphoma Chronic lymphocytic leukemia CAR-T therapy Allogeneic stem cell transplantation Autologous stem cell transplantation COVID-19 Respiratory virus Immunocompromised patients Moderna mRNA-1273 Pfizer-BioNTech BNT162b2

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Towards Computer-Assisted Curricula Design Using probabilistic graphical models 13th

Towards Computer-Assisted Curricula Design Using Probabilist...

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13th International Conference on Intelligent Tutoring Systems (ITS)

作者： Alsanie, Waleed Alkurtass, Issa Al-Hamoud, Abdullah King Abdulaziz City Sci & Technol Natl Ctr Computat Technol & Appl Math Riyadh Saudi Arabia Al Imam Mohammed Ibn Saud Islamic Univ Coll Comp & Informat Sci Riyadh Saudi Arabia

ISBN: (纸本)9783319395821;9783319395838

A conventional way of designing curricula is to ask domain experts to set dependencies between courses. A domain expert normally sets these dependencies based on the contents of the courses. Regardless of the disagreements amongst experts in deciding these dependencies, several studies suggest that data-driven approaches can be very effective. In this paper, we propose a system to induce independence/dependence assumptions between courses in curricula based on student performance.

关键词： Curricula design Educational tools Dependency graph Intelligent tutoring systems Machine learning probabilistic graphical models

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EMI Risk Estimation for System-Level Functions Using probabilistic graphical models

EMI Risk Estimation for System-Level Functions Using Probabi...

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IEEE International Joint Symposium on Electromagnetic Compatibility (EMC), Signal and Power Integrity (SIPI) and EMC Europe

作者： Devaraj, Lokesh Ruddle, Alastair R. Duffy, Alistair P. HORIBA MIRA Ltd Nuneaton England De Montfort Univ Leicester Leics England

ISBN: (纸本)9781665448888

In general, the functions provided by complex systems often involve multiple sub-systems and components that are functionally dependent on each other. The dependency could be to receive power, control signals, input data, memory storage, feedback etc. With the increasing use of electronic systems to perform critical functions, the potential for malfunctions due to electromagnetic interference need to be identified and mitigated. Hence, a risk analysis, estimating the likelihood and severity of electromagnetic interference effects, is desirable from the very early stages of system development. In this paper, the use of probabilistic graphical models for estimating the likelihood of electromagnetic disturbances causing system malfunctions with various degrees of severity is demonstrated using a very simple case study. Statistical data are synthesised to illustrate the construction of conditional probability distribution tables for a Bayesian Network system model. Factorization and inference techniques are then applied to demonstrate the formulation and answer of queries that could be of value during system risk assessment.

关键词： risk analysis probabilistic graphical models Bayesian network failure analysis electromagnetic interference

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Efficient Examination of Soil Bacteria Using probabilistic graphical models 1

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31st International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems (IEA/AIE)

作者： Butz, Cory J. dos Santos, Andre E. Oliveira, Jhonatan S. Stavrinides, John Univ Regina Dept Comp Sci Regina SK Canada Univ Regina Dept Biol Regina SK Canada

ISBN: (数字)9783319920580

ISBN: (纸本)9783319920580;9783319920573

This paper describes a novel approach to study bacterial relationships in soil datasets using probabilistic graphical models. We demonstrate how to access and reformat publicly available datasets in order to apply machine learning techniques. We first learn a Bayesian network in order to read independencies in linear time between bacterial community characteristics. These independencies are useful in understanding the semantic relationships between bacteria within communities. Next, we learn a Sum-Product network in order to perform inference in linear time. Here, inference can be conducted to answer traditional queries, involving posterior probabilities, or MPE queries, requesting the most likely values of the non-evidence variables given evidence. Our results extend the literature by showing that known relationships between soil bacteria holding in one or a few datasets in fact hold across at least 3500 diverse datasets. This study paves the way for future largescale studies of agricultural, health, and environmental applications, for which data are publicly available.

关键词： probabilistic graphical models Deep learning Soil Bacteria

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An Efficient Low-Rank Tensors Representation for Algorithms in Complex probabilistic graphical models 10

An Efficient Low-Rank Tensors Representation for Algorithms ...

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10th International Conference on probabilistic graphical models (PGM)

作者： Ducamp, Gaspard Bonnard, Philippe Nouy, Anthony Wuillemin, Pierre-Henri IBM France Lab 9 Rue Verdun F-94253 Gentilly France Cent Nantes LMJL 1 Rue Noe F-44321 Nantes France LIP6 4 Pl Jussieu F-75005 Paris France

probabilistic graphical models form a class of compact representations of high-dimensional probability distributions by decomposing these distributions in a set of multivariate factors (potentials). Every exact algorithm (for probabilistic inference, MAP, etc.) operates on a specific representation of these potentials. However complex probabilistic models often lead to very large potentials which dramatically impact both the space and time complexities of these algorithms and which can make inference in complex models intractable. In this paper we propose a new approach based on low-rank tensor representation to approximate and operate with these potentials. The low-rank tensor representation is used for the approximation of potentials with controlled precision and an important reduction in the number of parameters. Every operator used in such algorithms (multiplication, addition, projection, etc.) can be defined within this representation, leading to an approximation framework where every algorithm for PGMs can be easily implemented. As an instance of this framework, we present a classical message passing algorithm in Bayesian networks using the tensor train format. By reducing significantly the computational complexity and the memory usage, the proposed approach makes probabilistic inference much more scalable. These results are illustrated by experiments on dynamic Bayesian networks and classical Bayesian networks performed using a Python implementation with TensorFlow, T3F and pyAgrum.

关键词： probabilistic graphical models Approximate Inference Tensor Decomposition Low-rank approximation Tensor Train Format

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Tracking Performance and Forming Study Groups for Prep Courses Using probabilistic graphical models 15

Tracking Performance and Forming Study Groups for Prep Cours...

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15th International Conference on Autonomous Agents and Multiagent Systems (AAMAS)

作者： Bachrach, Yoram Lewenberg, Yoad Rosenschein, Jeffrey S. Zick, Yair Microsoft Res Cambridge England Hebrew Univ Jerusalem Jerusalem Israel Carnegie Mellon Univ Pittsburgh PA 15213 USA

ISBN: (纸本)9781450342391

Efficient tracking of class performance across topics is an important aspect of classroom teaching;this is especially true for psychometric general intelligence exams, which test a varied range of abilities. We develop a framework that uncovers a hidden thematic structure underlying student responses to a large pool of questions, using a probabilistic graphical model.

关键词： probabilistic graphical models Bayesian PCA Education

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Learning Semantic models of Data Sources Using probabilistic graphical models 19

Learning Semantic Models of Data Sources Using Probabilistic...

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World Wide Web Conference (WWW)

作者： Binh Vu Knoblock, Craig A. Pujara, Jay USC Informat Sci Inst Marina Del Rey CA 90292 USA

ISBN: (纸本)9781450366748

A semantic model of a data source is a representation of the concepts and relationships contained in the data. Building semantic models is a prerequisite to automatically publishing data to a knowledge graph. However, creating these semantic models is a complex process requiring considerable manual effort and can be error-prone. In this paper, we present a novel approach that efficiently searches over the combinatorial space of possible semantic models, and applies a probabilistic graphical model to identify the most probable semantic model for a data source. probabilistic graphical models offer many advantages over existing methods: they are robust to noisy inputs and provide a straightforward approach for exploiting relationships within the data. Our solution uses a conditional random field (CRF) to encode structural patterns and enforce conceptual consistency within the semantic model. In an empirical evaluation, our approach outperforms state of the art systems by an average 8.4% of F-1 score, even with noisy input data.

关键词： Semantic models knowledge graph probabilistic graphical models semantic web linked data ontology

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MAsCOT: Self-adaptive Opportunistic Offloading for Cloud-Enabled Smart Mobile Applications with probabilistic graphical models at Runtime 49

MAsCOT: Self-adaptive Opportunistic Offloading for Cloud-Ena...

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49th Hawaii International Conference on System Sciences (HICSS)

作者： Naqvi, Nayyab Zia Devlieghere, Jonas Preuveneers, Davy Berbers, Yolande Katholieke Univ Leuven iMinds DistriNet Leuven Belgium

ISBN: (纸本)9780769556703

Although extensive progress has been made in Mobile Cloud Augmentation, automated decision support on the device that enables the opportunistic and intelligent use of cloud resources is missing. Furthermore, we need solutions with reflective capabilities that can handle a changing environment and runtime variability. To simplify the deployment of smart mobile applications, we present a framework with retrospective decision support based on reinforcement learning to cater for various resource-performance trade-offs. We have adopted the MAPE-K (Monitor-Analyse-Plan-Execute-Knowledge) control loop architecture and realized the loop with Dynamic Decision Networks to manage self-adaptation at runtime. Our experiments show that our framework is capable of intelligently inferring appropriate decisions with an acceptable performance overhead of 10 milliseconds on mobile devices.

关键词： Mobile cloud computing decision support probabilistic graphical models dynamic offloading models@runtime

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Insights into Learning Competence Through probabilistic graphical models 1

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3rd IFIP TC 5, TC 12, WG 8.4, WG 8.9, WG 12.9 International Cross-Domain Conference for Machine Learning and Knowledge Extraction (CD-MAKE)

作者： Saranti, Anna Taraghi, Behnam Ebner, Martin Holzinger, Andreas Graz Univ Technol Dept Educ Technol Munzgrabenstr 36-1 A-8010 Graz Austria Graz Univ Technol Inst Interact Syst & Data Sci Inffeldgasse 16c-1 A-8010 Graz Austria Med Univ Graz Inst Med Informat Stat & Documentat Auenbruggerpl 2 A-8036 Graz Austria

ISBN: (数字)9783030297268

ISBN: (纸本)9783030297268;9783030297251

One-digit multiplication problems is one of the major fields in learning mathematics at the level of primary school that has been studied over and over. However, the majority of related work is focusing on descriptive statistics on data from multiple surveys. The goal of our research is to gain insights into multiplication misconceptions by applying machine learning techniques. To reach this goal, we trained a probabilistic graphical model of the students' misconceptions from data of an application for learning multiplication. The use of this model facilitates the exploration of insights into human learning competence and the personalization of tutoring according to individual learner's knowledge states. The detection of all relevant causal factors of the erroneous students answers as well as their corresponding relative weight is a valuable insight for teachers. Furthermore, the similarity between different multiplication problems - according to the students behavior - is quantified and used for their grouping into clusters. Overall, the proposed model facilitates real-time learning insights that lead to more informed decisions.

关键词： Bayesian networks probabilistic graphical models Learning analytics

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