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Label-Sensitive Task Grouping by Bayesian Nonparametric Approach for Multi-Task Multi-Label Learning

Xiao Zhang, Wenzhong Li, Vu Nguyen, Fuzhen Zhuang, Hui Xiong, Sanglu Lu
2018 Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence  
In this paper, we propose a LABel-sensitive TAsk Grouping framework, named LABTAG, based on Bayesian nonparametric approach for multi-task multi-label classification.  ...  The proposed framework explores the label correlations to capture feature-label patterns, and clusters similar tasks into groups with shared knowledge, which are learned jointly to produce a strengthened  ...  ., 2016] proposed a Bayesian nonparametric approach to learn the number of label-feature correlation patterns automatically.  ... 
doi:10.24963/ijcai.2018/434 dblp:conf/ijcai/ZhangLNZXL18 fatcat:m2tjnhfqs5czhod3shiuq73hnu

Nonparametric Online Machine Learning with Kernels

Khanh Nguyen
2017 Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence  
Our approach is to view max-margin and kernel methods under a Bayesian setting, then use Bayesian inference tools to learn model parameters and infer hyper-parameters in principle ways for both batch and  ...  It becomes urgent in online learning context. Grid search is a common approach, but it turns out to be highly problematic in real-world applications.  ...  A notable approach to relax the grid search is to use multiple kernel learning (MKL) [Gönen and Alpaydın, 2011] .  ... 
doi:10.24963/ijcai.2017/758 dblp:conf/ijcai/Nguyen17 fatcat:yeu276gfxbgyraxsptqggdvkya

Learning beyond Predefined Label Space via Bayesian Nonparametric Topic Modelling [article]

Changying Du, Fuzhen Zhuang, Jia He, Qing He, Guoping Long
2019 arXiv   pre-print
In this paper, we propose a Bayesian nonparametric topic model to automatically infer this number, based on the hierarchical Dirichlet process and the notion of latent Dirichlet allocation.  ...  Extensive experiments on various text data sets show that: (a) compared with parametric approaches that use pre-specified true number of new categories, the proposed nonparametric approach can yield comparable  ...  Algorithm 1 Collapsed Gibbs Sampling for LBPL-NTM Input: the words W, the number of topics L, parameter ζ of the base Dirichlet distribution H, the hyper-parameters β, aγ, bγ, aα, bα, and the maximal number  ... 
arXiv:1910.04420v1 fatcat:3gmtt5x4wreh5hi2e4qvokhul4

A unified view of generative models for networks: models, methods, opportunities, and challenges [article]

Abigail Z. Jacobs, Aaron Clauset
2014 arXiv   pre-print
Differences between models generally stem from different philosophical choices about how to learn from data or different empirically-motivated goals.  ...  For instance, novel theoretical models and optimization techniques developed in machine learning are largely unknown within the social and biological sciences, which have instead emphasized model interpretability  ...  For example, how can we understand what is being traded off when we use a latent block model under a Bayesian nonparametric learning paradigm versus a general latent space model under a frequentist learning  ... 
arXiv:1411.4070v1 fatcat:6z2nwwdxlnhb5cxflpprgosmza

Predicting Market Impact Costs Using Nonparametric Machine Learning Models

Saerom Park, Jaewook Lee, Youngdoo Son, Yingfeng Zhang
2016 PLoS ONE  
In this paper, we employed the state-of-the-art nonparametric machine learning models: neural networks, Bayesian neural network, Gaussian process, and support vector regression, to predict market impact  ...  As a result, most nonparametric machine learning models outperformed a-state-of-the-art benchmark parametric model such as I-star model in four error measures.  ...  Acknowledgments The data set used in this work was collected by using the Bloomberg Terminal, which can be accessed by subscribing to the Bloomberg Professional service.  ... 
doi:10.1371/journal.pone.0150243 pmid:26926235 pmcid:PMC4771170 fatcat:jptiayliuvgvfhvb4wkpmnmdgm

Nonparametric Bayesian Modeling for Automated Database Schema Matching [article]

Erik M. Ferragut, Jason Laska
2015 arXiv   pre-print
Our experiments show that our method is more accurate and faster than the existing instance-based matching algorithms in part because of the use of nonparametric Bayesian models.  ...  We introduce a schema matching framework that builds nonparametric Bayesian models for each field and compares them by computing the probability that a single model could have generated both fields.  ...  Finally, we show by experiments that the success of our approach is attributable (at least in part) to the properties of nonparametric Bayesian models. Sensitivity to Data Size.  ... 
arXiv:1507.01443v1 fatcat:zbx35dh24nhkjapptjvm6qfpve

The Indian Chefs Process [article]

Patrick Dallaire, Luca Ambrogioni, Ludovic Trottier, Umut Güçlü, Max Hinne, Philippe Giguère, Brahim Chaib-Draa, Marcel van Gerven, Francois Laviolette
2020 arXiv   pre-print
To the best of our knowledge, the ICP is the first Bayesian nonparametric model supporting every possible DAG.  ...  This paper introduces the Indian Chefs Process (ICP), a Bayesian nonparametric prior on the joint space of infinite directed acyclic graphs (DAGs) and orders that generalizes Indian Buffet Processes.  ...  To our knowledge, no Bayesian nonparametric structure learning methods have been applied to deep learning models.  ... 
arXiv:2001.10657v1 fatcat:dg4qrbeckfeklh5ile2a647bnq

An Infinite Mixture of Inverted Dirichlet Distributions [chapter]

Taoufik Bdiri, Nizar Bouguila
2011 Lecture Notes in Computer Science  
The proposed mixture is learned using a fully Bayesian approach and allows to overcome a challenging issue when dealing with data clustering namely the automatic selection of the number of clusters.  ...  The results show that the proposed approach is effective for positive data modeling when compared to those reported using infinite Gaussian mixture.  ...  The goal of this paper is to extend our work in [7] to the infinite case by considering a nonparametric Bayesian approach namely Dirichlet processes which have a history going back to Antoniak [8] .  ... 
doi:10.1007/978-3-642-24958-7_9 fatcat:2koqx5jfmzd3tppeu6iw6gfyt4

Categorization as nonparametric Bayesian density estimation [chapter]

Thomas L. Griffiths, Adam N. Sanborn, Kevin R. Canini, Daniel J. Navarro
2008 The Probabilistic Mind:  
Categorization as nonparametric Bayesian density estimation 2 Categorization as nonparametric Bayesian density estimation Rational models of cognition aim to explain the structure of human thought and  ...  In this chapter, we pursue a rational analysis of category learning: inferring the structure of categories from a set of stimuli labeled as belonging to those categories.  ...  From a Bayesian perspective, the nonparametric approach requires us to use priors that include as broad a range of densities of possible, thereby allowing us to infer very complex densities if they are  ... 
doi:10.1093/acprof:oso/9780199216093.003.0014 fatcat:g5y7fmeehngtbniymjkqcpp5h4

Bayesian Nonparametric View to Spawning [article]

Bahman Moraffah
2021 arXiv   pre-print
In this paper, we introduce a novel Bayesian nonparametric approach that models a scenario where each observation may be drawn from an unknown number of objects for which it provides a tractable Markov  ...  Therefore, the association of each measurement to multiple objects is a crucial task to perform in order to track multiple objects with birth and death.  ...  Due to the efficiency of Bayesian nonparametric modeling, in [1] - [4] , authors employed nonparametric Bayesian modeling to improve the tracking of multiple objects.  ... 
arXiv:2112.06640v1 fatcat:des52vsbezggpc6wlxrqbka67u

Probabilistic Machine Learning: Models, Algorithms and a Programming Library

Jun Zhu
2018 Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence  
In this paper, we present an overview of our recent work on probabilistic machine learning, including the theory of regularized Bayesian inference, Bayesian deep learning, scalable inference algorithms  ...  , a probabilistic programming library named ZhuSuan, and applications in representation learning as well as learning from crowds.  ...  We built a nonparametric Bayesian model to identify worker reliability and task clarity without the assumption of ground truth labels [Tian and Zhu, 2012] .  ... 
doi:10.24963/ijcai.2018/823 dblp:conf/ijcai/Zhu18 fatcat:6cnqt2gmg5abre73cac7w2ya6y

Nonparametric Variational Auto-encoders for Hierarchical Representation Learning [article]

Prasoon Goyal, Zhiting Hu, Xiaodan Liang, Chenyu Wang, Eric Xing
2017 arXiv   pre-print
In this work, we propose hierarchical nonparametric variational autoencoders, which combines tree-structured Bayesian nonparametric priors with VAEs, to enable infinite flexibility of the latent representation  ...  The resulting model induces a hierarchical structure of latent semantic concepts underlying the data corpus, and infers accurate representations of data instances.  ...  FA8702-15-D-0002 with Carnegie Mellon University for the operation of the Software Engineering Institute, a federally funded research and development center.  ... 
arXiv:1703.07027v2 fatcat:fllrzupobrfhjpjeejjgzgmve4

Bayesian Nonparametric Learning of How Skill Is Distributed across the Mutual Fund Industry

Mark Jensen, Mark Fisher, Paula Tkac
2019 Federal Reserve Bank of Atlanta, Working Papers  
Applying our Bayesian nonparametric learning approach to a panel of actively managed, domestic equity funds, we find the population distribution of skill to be fat-tailed, skewed towards higher levels  ...  In this paper, we use Bayesian nonparametric learning to estimate the skill of actively managed mutual funds and also to estimate the population distribution for this skill.  ...  In Section 6 we apply our Bayesian nonparametric learning approach along with a Bayesian parametric hierarchical model and a idiosyncratic Bayesian parametric model to a panel of 5,136 actively managed  ... 
doi:10.29338/wp2019-03 fatcat:pmmqyxdbg5cqdgrdnylprjqybq

Spatiotemporal Learning of Multivehicle Interaction Patterns in Lane-Change Scenarios [article]

Chengyuan Zhang, Jiacheng Zhu, Wenshuo Wang, Junqiang Xi
2020 arXiv   pre-print
Then, a discrete Bayesian nonparametric model, integrating Dirichlet processes and hidden Markov models, is developed to learn the interaction patterns over the temporal space by segmenting and clustering  ...  This paper describes a Bayesian nonparametric approach that leverages continuous (i.e., Gaussian processes) and discrete (i.e., Dirichlet processes) stochastic processes to reveal underlying interaction  ...  We first revisit the basic concept of primitives and then introduce the related Bayesian nonparametric methods to learn them. A.  ... 
arXiv:2003.00759v2 fatcat:wygdmojgvndixbrez2hblmijpq

The Future of Data Analysis in the Neurosciences [article]

Danilo Bzdok, B. T. Thomas Yeo
2016 arXiv   pre-print
While growing data availability and information granularity have been amply discussed, we direct attention to a routinely neglected question: How will the unprecedented data richness shape data analysis  ...  We believe that large-scale data analysis will use more models that are non-parametric, generative, mixing frequentist and Bayesian aspects, and grounded in different statistical inferences.  ...  Instead of fitting one parameter to each variable to predict a behavior or clinical outcome with linear regression, GPs learn a nonparametric distribution of non-linear functions to explain brain-behavior  ... 
arXiv:1608.03465v1 fatcat:roen4d2axncufftj3ifjjimqpe
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