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Robust Particle Filtering via Bayesian Nonparametric Outlier Modeling

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arXiv 2018年

作者： Liu, Bin School of Computer Science Jiangsu Key Lab of Big Data Security & Intelligent Processing Nanjing University of Posts and Telecommunications Nanjing China

This paper is concerned with the online estimation of a nonlinear dynamic system from a series of noisy measurements. The focus is on cases wherein outliers are present in-between normal noises. We assume that the outliers follow an unknown generating mechanism which deviates from that of normal noises, and then model the outliers using a Bayesian nonparametric model called Dirichlet process mixture (DPM). A sequential particle-based algorithm is derived for posterior inference for the outlier model as well as the state of the system to be estimated. The resulting algorithm is termed DPM based robust PF (DPM-RPF). The nonparametric feature makes this algorithm allow the data to "speak for itself" to determine the complexity and structure of the outlier model. Simulation results show that it performs remarkably better than two state-of-the-art methods especially when outliers appear frequently along time. Copyright © 2018, The Authors. All rights reserved.

关键词： Statistics

Harnessing Low-Fidelity data to Accelerate Bayesian Optimization via Posterior Regularization

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Harnessing Low-Fidelity Data to Accelerate Bayesian Optimiza...

International Conference on big data and Smart Computing (bigCOMP)

作者： Bin Liu School of Computer Science Jiangsu Key Lab of Big Data Security & Intelligent Processing Nanjing University of Posts and Telecommunications Nanjing China

ISBN: (数字)9781728160344

ISBN: (纸本)9781728160351

Bayesian optimization (BO) is a powerful paradigm for derivative-free global optimization of a black-box objective function (BOF) that is expensive to evaluate. However, the overhead of BO can still be prohibitive for problems with highly expensive function evaluations. In this paper, we investigate how to reduce the required number of function evaluations for BO without compromise in solution quality. We explore the idea of posterior regularization to harness low fidelity (LF) data within the Gaussian process upper confidence bound (GP-UCB) framework. The LF data can arise from previous evaluations of an LF approximation of the BOF or a related optimization task. An extra GP model called LF-GP is trained to fit the LF data. We develop an operator termed dynamic weighted product of experts (DW-POE) fusion. The regularization is induced by this operator on the posterior of the BOF. The impact of the LF GP model on the resulting regularized posterior is adaptively adjusted via Bayesian formalism. Extensive experimental results on benchmark BOF optimization tasks demonstrate the superior performance of the proposed algorithm over state-of-the-art.

关键词： Optimization Hafnium Adaptation models Bayes methods Heuristic algorithms data models Acceleration

Decomposing Source Codes by Program Slicing for Bug Localization

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Decomposing Source Codes by Program Slicing for Bug Localiza...

International Joint Conference on Neural Networks (IJCNN)

作者： Jian Yong Ziye Zhu Yun Li Jiangsu Key Laboratory of Big Data Security and Intelligent Processing School of Computer Science Nanjing University of Posts and Telecommunications

Bug localization, which aims to automatically locate buggy source code files based on the given bug report, is a critical yet time-consuming task in the software engineering field. Existing advanced bug localization methods have successfully leveraged deep learning to bridge the lexical gap between bug reports and source code files at the semantic level. These methods usually first build the entire source code file semantic representation and then match it with the bug report. However, the bug described in the bug report may be related to only part of the source code file semantics. Directly constructing a semantic representation of the entire source code file would increase the difficulty of semantic matching between bug reports and source code files. In this paper, we propose a novel model named S-BugLocator, which decomposes source code file with the help of program slicing. Especially, our proposed S-BugLocator incorporates two distinctly structured slice feature extraction components in processing source code files to cope with the significant discrepancy between multi-row slices and single-row slices. For each multi-row slice, a CNN and Bi-LSTM network is firstly employed to extract its semantics and then a keywords supervised attention mechanism is designed to build its semantic representation by focusing on slices that have strong relevance with the bug report. For each single-row slice, the semantic representation is obtained by fusing word embeddings in single-row slices. The experimental results on four real-world large-scale projects indicate that our proposed model outperforms existing state-of-the-art bug localization methods.

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Harnessing low-fidelity data to accelerate Bayesian optimization via posterior regularization

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arXiv 2019年

作者： Liu, Bin School of Computer Science Jiangsu Key Lab of Big Data Security & Intelligent Processing Nanjing University of Posts and Telecommunications Nanjing China

关键词： Function evaluation

Robust Particle Filtering via Bayesian Nonparametric Outlier Modeling (Poster)

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Robust Particle Filtering via Bayesian Nonparametric Outlier...

International Conference on Information Fusion

作者： Bin Liu School of Computer Science Jiangsu Key Lab of Big Data Security & Intelligent Processing Nanjing University of Posts and Telecommunications Nanjing China

ISBN: (数字)9780996452786

ISBN: (纸本)9781728118406

This paper is concerned with the online estimation of a nonlinear dynamic system from a series of noisy measurements. The focus is on cases wherein outliers are present in-between normal noises. We assume that the outliers follow an unknown generating mechanism which deviates from that of normal noises, and then model the outliers using a Bayesian nonparametric model called Dirichlet process mixture (DPM). A sequential particle-based algorithm is derived for posterior inference for the outlier model as well as the state of the system to be estimated. The resulting algorithm is termed DPM based robust PF (DPM-RPF). The nonparametric feature makes this algorithm allow the data to “speak for itself” to determine the complexity and structure of the outlier model. Simulation results show that it performs remarkably better than two state-of-the-art methods especially when outliers appear frequently along time.

关键词： Noise measurement Inference algorithms Bayes methods Computational modeling Atmospheric measurements Particle measurements Pollution measurement

Particle filtering methods for stochastic optimization with application to large-scale empirical risk minimization

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arXiv 2018年

作者： Liu, Bin School of Computer Science Nanjing University of Posts and Telecommunications Jiangsu Key Lab of Big Data Security & Intelligent Processing Nanjing Jiangsu210023 China

There is a recent interest in developing statistical filtering methods for stochastic optimization (FSO) by leveraging a probabilistic perspective of incremental proximity methods (IPMs). The existent FSO methods are derived based on the Kalman filter (KF) and extended KF (EKF). Different from classical stochastic optimization methods such as the stochastic gradient descent (SGD) and typical IPMs, such KF-type algorithms possess a desirable property, namely they do not require pre-scheduling of the learning rate for convergence. However, on the other side, they have inherent limitations inherited from the nature of KF mechanisms. It is a consensus that the class of particle filters (PFs) outperforms the KF and its variants remarkably for nonlinear and/or non-Gaussian statistical filtering tasks. Hence, it is natural to ask if the FSO methods can benefit from the PF theory to get around of limitations of the KF-type stochastic optimization methods. We provide an affirmative answer to the aforementioned question by developing two PF based stochastic optimizers (PFSOs). For performance evaluation, we apply them to address nonlinear least-square fitting using simulated data sets and empirical risk minimization for binary classification using real data sets. Experimental results demonstrate that PFSOs outperform remarkably existent methods in terms of numerical stability, convergence speed, and flexibility in handling different types of loss functions. Copyright © 2018, The Authors. All rights reserved.

关键词： Monte Carlo methods

ILAPF: INCREMENTAL LEARNING ASSISTED PARTICLE FILTERING

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arXiv 2017年

作者： Liu, Bin School of Computer Science Jiangsu Key Lab of Big Data Security & Intelligent Processing Nanjing University of Posts and Telecommunications Nanjing210023 China

This paper is concerned with dynamic system state estimation based on a series of noisy measurement with the presence of outliers. An incremental learning assisted particle filtering (ILAPF) method is presented, which can learn the value range of outliers incrementally during the process of particle filtering. The learned range of outliers is then used to improve subsequent filtering of the future state. Convergence of the outlier range estimation procedure is indicated by extensive empirical simulations using a set of differing outlier distribution models. The validity of the ILAPF algorithm is evaluated by illustrative simulations, and the result shows that ILAPF is more accurate and faster than a recently published state-of-the-art robust particle filter. It also shows that the incremental learning property of the ILAPF algorithm provides an efficient way to implement transfer learning among related state filtering tasks. Copyright © 2017, The Authors. All rights reserved.

关键词： Monte Carlo methods

ILAPF: INCREMENTAL LEARNING ASSISTED PARTICLE FILTERING

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ILAPF: INCREMENTAL LEARNING ASSISTED PARTICLE FILTERING

IEEE International Conference on Acoustics, Speech and Signal processing

作者： Bin Liu School of Computer Science Jiangsu Key Lab of Big Data Security & Intelligent Processing Nanjing University of Posts and Telecommunications Nanjing 210023 China

ISBN: (纸本)9781538646595

关键词： incremental learning outlier particle filtering robust state filtering transfer learning outliers Transfer (Psychology) Particle filters learning (artificial intelligence) Filtration State estimation

data-DRIVEN MODEL SET DESIGN FOR MODEL AVERAGED PARTICLE FILTER

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arXiv 2019年

作者： Liu, Bin School of Computer Science Jiangsu Key Lab of Big Data Security & Intelligent Processing Nanjing University of Posts and Telecommunications Nanjing210023 China

This paper is concerned with sequential state filtering in the presence of nonlinearity, non-Gaussianity and model uncertainty. For this problem, the Bayesian model averaged particle filter (BMAPF) is perhaps one of the most efficient solutions. Major advances of BMAPF have been made, while it still lacks a generic and practical approach to design the model set. This paper fills in this gap by proposing a generic data-driven method for BMAPF model set design. Unlike existent methods, the proposed solution does not require any prior knowledge on the parameter value of the true model;it only assumes that a small number of noisy observations are pre-obtained. The Bayesian optimization (BO) method is adapted to search the model components, each of which is associated with a specific segment of the pre-obtained data set. The average performance of these model components is guaranteed since each one’s parameter value is elaborately tuned via BO to maximize the marginal likelihood. The diversity in the model components is also ensured, as different components match the different segments of the pre-obtained data set, respectively. computer simulations are used to demonstrate the effectiveness of the proposed method. Copyright © 2019, The Authors. All rights reserved.

关键词： Monte Carlo methods