Bibliografie tematiche / Kernel Inference

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Letteratura scientifica selezionata sul tema "Kernel Inference"

Autore: Grafiati

Pubblicato: 7 luglio 2024

Ultima modifica: 19 luglio 2025

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Articoli di riviste sul tema "Kernel Inference"

Nishiyama, Yu, Motonobu Kanagawa, Arthur Gretton, and Kenji Fukumizu. "Model-based kernel sum rule: kernel Bayesian inference with probabilistic models." Machine Learning 109, no. 5 (2020): 939–72. http://dx.doi.org/10.1007/s10994-019-05852-9.

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AbstractKernel Bayesian inference is a principled approach to nonparametric inference in probabilistic graphical models, where probabilistic relationships between variables are learned from data in a nonparametric manner. Various algorithms of kernel Bayesian inference have been developed by combining kernelized basic probabilistic operations such as the kernel sum rule and kernel Bayes’ rule. However, the current framework is fully nonparametric, and it does not allow a user to flexibly combine nonparametric and model-based inferences. This is inefficient when there are good probabilistic mod

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Rogers, Mark F., Colin Campbell, and Yiming Ying. "Probabilistic Inference of Biological Networks via Data Integration." BioMed Research International 2015 (2015): 1–9. http://dx.doi.org/10.1155/2015/707453.

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There is significant interest in inferring the structure of subcellular networks of interaction. Here we consider supervised interactive network inference in which a reference set of known network links and nonlinks is used to train a classifier for predicting new links. Many types of data are relevant to inferring functional links between genes, motivating the use of data integration. We use pairwise kernels to predict novel links, along with multiple kernel learning to integrate distinct sources of data into a decision function. We evaluate various pairwise kernels to establish which are mos

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LUGO-MARTINEZ, JOSE, and PREDRAG RADIVOJAC. "Generalized graphlet kernels for probabilistic inference in sparse graphs." Network Science 2, no. 2 (2014): 254–76. http://dx.doi.org/10.1017/nws.2014.14.

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AbstractGraph kernels for learning and inference on sparse graphs have been widely studied. However, the problem of designing robust kernel functions that can effectively compare graph neighborhoods in the presence of noisy and complex data remains less explored. Here we propose a novel graph-based kernel method referred to as an edit distance graphlet kernel. The method was designed to add flexibility in capturing similarities between local graph neighborhoods as a means of probabilistically annotating vertices in sparse and labeled graphs. We report experiments on nine real-life data sets fr

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Lazarus, Eben, Daniel J. Lewis, and James H. Stock. "The Size‐Power Tradeoff in HAR Inference." Econometrica 89, no. 5 (2021): 2497–516. http://dx.doi.org/10.3982/ecta15404.

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Heteroskedasticity‐ and autocorrelation‐robust (HAR) inference in time series regression typically involves kernel estimation of the long‐run variance. Conventional wisdom holds that, for a given kernel, the choice of truncation parameter trades off a test's null rejection rate and power, and that this tradeoff differs across kernels. We formalize this intuition: using higher‐order expansions, we provide a unified size‐power frontier for both kernel and weighted orthonormal series tests using nonstandard “fixed‐ b” critical values. We also provide a frontier for the subset of these tests for w

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Billio, M. "Kernel-Based Indirect Inference." Journal of Financial Econometrics 1, no. 3 (2003): 297–326. http://dx.doi.org/10.1093/jjfinec/nbg014.

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Zhang, Li Lyna, Shihao Han, Jianyu Wei, Ningxin Zheng, Ting Cao, and Yunxin Liu. "nn-METER." GetMobile: Mobile Computing and Communications 25, no. 4 (2022): 19–23. http://dx.doi.org/10.1145/3529706.3529712.

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Inference latency has become a crucial metric in running Deep Neural Network (DNN) models on various mobile and edge devices. To this end, latency prediction of DNN inference is highly desirable for many tasks where measuring the latency on real devices is infeasible or too costly. Yet it is very challenging and existing approaches fail to achieve a high accuracy of prediction, due to the varying model-inference latency caused by the runtime optimizations on diverse edge devices. In this paper, we propose and develop nn-Meter, a novel and efficient system to accurately predict the DNN inferenc

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Robinson, P. M. "INFERENCE ON NONPARAMETRICALLY TRENDING TIME SERIES WITH FRACTIONAL ERRORS." Econometric Theory 25, no. 6 (2009): 1716–33. http://dx.doi.org/10.1017/s0266466609990302.

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The central limit theorem for nonparametric kernel estimates of a smooth trend, with linearly generated errors, indicates asymptotic independence and homoskedasticity across fixed points, irrespective of whether disturbances have short memory, long memory, or antipersistence. However, the asymptotic variance depends on the kernel function in a way that varies across these three circumstances, and in the latter two it involves a double integral that cannot necessarily be evaluated in closed form. For a particular class of kernels, we obtain analytic formulas. We discuss extensions to more gener

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Yuan, Ao. "Semiparametric inference with kernel likelihood." Journal of Nonparametric Statistics 21, no. 2 (2009): 207–28. http://dx.doi.org/10.1080/10485250802553382.

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Cheng, Yansong, and Surajit Ray. "Multivariate Modality Inference Using Gaussian Kernel." Open Journal of Statistics 04, no. 05 (2014): 419–34. http://dx.doi.org/10.4236/ojs.2014.45041.

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Agbokou, Komi, and Yaogan Mensah. "INFERENCE ON THE REPRODUCING KERNEL HILBERT SPACES." Universal Journal of Mathematics and Mathematical Sciences 15 (October 10, 2021): 11–29. http://dx.doi.org/10.17654/2277141722002.

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Tesi sul tema "Kernel Inference"

Fouchet, Arnaud. "Kernel methods for gene regulatory network inference." Thesis, Evry-Val d'Essonne, 2014. http://www.theses.fr/2014EVRY0058/document.

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De nouvelles technologies, notamment les puces à adn, multiplient la quantité de données disponibles pour la biologie moléculaire. dans ce contexte, des méthodes informatiques et mathématiques sont activement développées pour extraire le plus d'information d'un grand nombre de données. en particulier, le problème d'inférence de réseaux de régulation génique a été abordé au moyen de multiples modèles mathématiques et statistiques, des plus basiques (corrélation, modèle booléen ou linéaire) aux plus sophistiqués (arbre de régression, modèles bayésiens avec variables cachées). malgré leurs qualit

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Chan, Karen Pui-Shan. "Kernel density estimation, Bayesian inference and random effects model." Thesis, University of Edinburgh, 1990. http://hdl.handle.net/1842/13350.

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This thesis contains results of a study in kernel density estimation, Bayesian inference and random effects models, with application to forensic problems. Estimation of the Bayes' factor in a forensic science problem involved the derivation of predictive distributions in non-standard situations. The distribution of the values of a characteristic of interest among different items in forensic science problems is often non-Normal. Background, or training, data were available to assist in the estimation of the distribution for measurements on cat and dog hairs. An informative prior, based on the k

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Araya, Valdivia Ernesto. "Kernel spectral learning and inference in random geometric graphs." Thesis, université Paris-Saclay, 2020. http://www.theses.fr/2020UPASM020.

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Cette thèse comporte deux objectifs. Un premier objectif concerne l’étude des propriétés de concentration des matrices à noyau, qui sont fondamentales dans l’ensemble des méthodes à noyau. Le deuxième objectif repose quant à lui sur l’étude des problèmes d’inférence statistique dans le modèle des graphes aléatoires géométriques. Ces deux objectifs sont liés entre eux par le formalisme du graphon, qui permet représenter un graphe par un noyau. Nous rappelons les rudiments du modèle du graphon dans le premier chapitre. Le chapitre 2 présente des bornes précises pour les valeurs propres individue

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Jitkrittum, Wittawat. "Kernel-based distribution features for statistical tests and Bayesian inference." Thesis, University College London (University of London), 2017. http://discovery.ucl.ac.uk/10037987/.

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The kernel mean embedding is known to provide a data representation which preserves full information of the data distribution. While typically computationally costly, its nonparametric nature has an advantage of requiring no explicit model specification of the data. At the other extreme are approaches which summarize data distributions into a finite-dimensional vector of hand-picked summary statistics. This explicit finite-dimensional representation offers a computationally cheaper alternative. Clearly, there is a trade-off between cost and sufficiency of the representation, and it is of inter

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Hsu, Yuan-Shuo Kelvin. "Bayesian Perspectives on Conditional Kernel Mean Embeddings: Hyperparameter Learning and Probabilistic Inference." Thesis, University of Sydney, 2020. https://hdl.handle.net/2123/24309.

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This thesis presents the narrative of a particular journey towards discovering and developing Bayesian perspectives on conditional kernel mean embeddings. It is motivated by the desire and need to learn flexible and richer representations of conditional distributions for probabilistic inference in various contexts. While conditional kernel mean embeddings are able to achieve such representations, it is unclear how their hyperparameters can be learned for probabilistic inference in various settings. These hyperparameters govern the space of possible representations, and critically influence the

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Adams, R. P. "Kernel methods for nonparametric Bayesian inference of probability densities and point processes." Thesis, University of Cambridge, 2009. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.595350.

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I propose two new kernel-based models that enable an exact generative procedure: the Gaussian process density sampler (GPDS) for probability density functions, and the sigmoidal Gaussian Cox process (SGCP) for the Poisson process. With generative priors, I show how it is now possible to construct two different kinds of Markov chains for inference in these models. These Markov chains have the desired posterior distribution as their equilibrium distributions, and, despite a parameter space with uncountably many dimensions, require only a finite amount of computation to simulate. The GPDS and SGC

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Gogolashvili, Davit. "Global and local Kernel methods for dataset shift, scalable inference and optimization." Electronic Thesis or Diss., Sorbonne université, 2022. https://accesdistant.sorbonne-universite.fr/login?url=https://theses-intra.sorbonne-universite.fr/2022SORUS363v2.pdf.

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Dans de nombreux problèmes du monde réel, les données de formation et les données de test ont des distributions différentes. Cette situation est communément appelée " décalage de l'ensemble de données ". Les paramètres les plus courants pour le décalage des ensembles de données souvent considérés dans la littérature sont le décalage des covariables et le décalage des cibles. Dans cette thèse, nous étudions les modèles nonparamétriques appliqués au scénario de changement d'ensemble de données. Nous développons un nouveau cadre pour accélérer la régression par processus gaussien. En particulier,

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Maity, Arnab. "Efficient inference in general semiparametric regression models." [College Station, Tex. : Texas A&M University, 2008. http://hdl.handle.net/1969.1/ETD-TAMU-3075.

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Minnier, Jessica. "Inference and Prediction for High Dimensional Data via Penalized Regression and Kernel Machine Methods." Thesis, Harvard University, 2012. http://dissertations.umi.com/gsas.harvard:10327.

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Analysis of high dimensional data often seeks to identify a subset of important features and assess their effects on the outcome. Furthermore, the ultimate goal is often to build a prediction model with these features that accurately assesses risk for future subjects. Such statistical challenges arise in the study of genetic associations with health outcomes. However, accurate inference and prediction with genetic information remains challenging, in part due to the complexity in the genetic architecture of human health and disease. A valuable approach for improving prediction models with a lar

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Weller, Jennifer N. "Bayesian Inference In Forecasting Volcanic Hazards: An Example From Armenia." [Tampa, Fla.] : University of South Florida, 2004. http://purl.fcla.edu/fcla/etd/SFE0000485.

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Libri sul tema "Kernel Inference"

Fauzi, Rizky Reza, and Yoshihiko Maesono. Statistical Inference Based on Kernel Distribution Function Estimators. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-1862-1.

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Silva, Catarina. Inductive inference for large scale text classification: Kernel approaches and techniques. Springer, 2010.

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C, Jones M., ed. Kernel smoothing. Chapman & Hall, 1995.

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Causal Inference from Statistical Data. Logos-Verlag Berlin, 2008.

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Fauzi, Rizky Reza, and Yoshihiko Maesono. Statistical Inference Based on Kernel Distribution Function Estimators. Springer, 2023.

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Silva, Catarina, and Bernadete Ribeiro. Inductive Inference for Large Scale Text Classification: Kernel Approaches and Techniques. Springer, 2012.

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Jones, M. C., and M. P. Wand. Kernel Smoothing. Taylor & Francis Group, 1994.

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Jones, M. C., and M. P. Wand. Kernel Smoothing. Taylor & Francis Group, 1994.

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Kernel Smoothing. 1995.

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Capitoli di libri sul tema "Kernel Inference"

Vovk, Vladimir. "Kernel Ridge Regression." In Empirical Inference. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-41136-6_11.

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Fauzi, Rizky Reza, and Yoshihiko Maesono. "Kernel Quantile Estimation." In Statistical Inference Based on Kernel Distribution Function Estimators. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-1862-1_3.

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Fauzi, Rizky Reza, and Yoshihiko Maesono. "Kernel Density Function Estimator." In Statistical Inference Based on Kernel Distribution Function Estimators. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-1862-1_1.

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Fauzi, Rizky Reza, and Yoshihiko Maesono. "Kernel Distribution Function Estimator." In Statistical Inference Based on Kernel Distribution Function Estimators. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-1862-1_2.

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Fauzi, Rizky Reza, and Yoshihiko Maesono. "Kernel-Based Nonparametric Tests." In Statistical Inference Based on Kernel Distribution Function Estimators. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-1862-1_5.

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Silva, Catarina, and Bernardete Ribeiro. "Kernel Machines for Text Classification." In Inductive Inference for Large Scale Text Classification. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-04533-2_2.

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Vert, Jean-Philippe. "Classification of Biological Sequences with Kernel Methods." In Grammatical Inference: Algorithms and Applications. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11872436_2.

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Christmann, Andreas, and Robert Hable. "On the Consistency of the Bootstrap Approach for Support Vector Machines and Related Kernel-Based Methods." In Empirical Inference. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-41136-6_20.

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Fukumizu, Kenji. "Nonparametric Bayesian Inference with Kernel Mean Embedding." In Modern Methodology and Applications in Spatial-Temporal Modeling. Springer Japan, 2015. http://dx.doi.org/10.1007/978-4-431-55339-7_1.

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Fauzi, Rizky Reza, and Yoshihiko Maesono. "Mean Residual Life Estimator." In Statistical Inference Based on Kernel Distribution Function Estimators. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-1862-1_4.

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Atti di convegni sul tema "Kernel Inference"

Chen, Weiteng, Yu Hao, Zheng Zhang, et al. "SyzGen++: Dependency Inference for Augmenting Kernel Driver Fuzzing." In 2024 IEEE Symposium on Security and Privacy (SP). IEEE, 2024. http://dx.doi.org/10.1109/sp54263.2024.00269.

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Neumann, Felix, Frederik Deroo, Georg Von Wichert, and Darius Burschka. "Particle-Based Dynamic Semantic Occupancy Mapping Using Bayesian Generalized Kernel Inference." In 2024 IEEE 27th International Conference on Intelligent Transportation Systems (ITSC). IEEE, 2024. https://doi.org/10.1109/itsc58415.2024.10920259.

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Kim, Junyoung, Junwon Seo, and Jihong Min. "Evidential Semantic Mapping in Off-road Environments with Uncertainty-aware Bayesian Kernel Inference." In 2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2024. https://doi.org/10.1109/iros58592.2024.10802766.

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Song, Yingchen, Yaobin Wang, Chaoyu Xiong, Tianhai Wang, and Pingping Tang. "An Efficient Sampling-Based SpMM Kernel for Balancing Accuracy and Speed in GNN Inference." In 2024 IEEE International Symposium on Parallel and Distributed Processing with Applications (ISPA). IEEE, 2024. https://doi.org/10.1109/ispa63168.2024.00066.

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Armeniakos, Giorgos, Georgios Mentzos, and Dimitrios Soudris. "Accelerating TinyML Inference on Microcontrollers Through Approximate Kernels." In 2024 31st IEEE International Conference on Electronics, Circuits and Systems (ICECS). IEEE, 2024. https://doi.org/10.1109/icecs61496.2024.10848979.

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Krajsek, Kai, and Hanno Scharr. "Bayesian inference in kernel feature space." In BAYESIAN INFERENCE AND MAXIMUM ENTROPY METHODS IN SCIENCE AND ENGINEERING: 31st International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering. AIP, 2012. http://dx.doi.org/10.1063/1.3703633.

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Sigal, L., R. Memisevic, and D. J. Fleet. "Shared Kernel Information Embedding for discriminative inference." In 2009 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2009. http://dx.doi.org/10.1109/cvprw.2009.5206576.

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Castro, Ivan, Cristobal Silva, and Felipe Tobar. "Initialising kernel adaptive filters via probabilistic inference." In 2017 22nd International Conference on Digital Signal Processing (DSP). IEEE, 2017. http://dx.doi.org/10.1109/icdsp.2017.8096055.

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Sigal, Leonid, Roland Memisevic, and David J. Fleet. "Shared Kernel Information Embedding for discriminative inference." In 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPR Workshops). IEEE, 2009. http://dx.doi.org/10.1109/cvpr.2009.5206576.

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Doherty, Kevin, Jinkun Wang, and Brendan Englot. "Bayesian generalized kernel inference for occupancy map prediction." In 2017 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2017. http://dx.doi.org/10.1109/icra.2017.7989356.

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