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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Qingming Li Zhanjiang Chen H. Vicky Zhao Yan Lindsay Sun Dept. of Automation Tsinghua Univ. State Key Lab of Intelligent Tech. & Sys. Tsinghua National Laboratory for Info. Sci. and Tech. Beijing P.R. China Dept. of Electrical Computer and Biomedical Engineering Univ. of Rhode Island USA

ISBN: (纸本)9781538646595

This paper proposes PRIMA: Probabilistic Ranking with Inter-item competition and Multi-Attribute utility function, which ranks items based on their probabilities of being a user's best choice. This framework is particularly important in E-commerce applications for making recommendations, predicting sales, and developing pricing strategies. To achieve mathematical tractability, it uses the weight-based multi-attribute utility function to address the inter-attribute tradeoff, where the weight reflects a user's personal preference for each attribute. The proposed work updates the weight from a user's past transactions using the concept of marginal rate of substitution from microeconomics, addresses the interitem competition, and computes the items' probabilities of being a user's best choice. Real user test results show that the proposed framework achieves comparable ranking accuracy to the state-of-the-art work with significant improvements in model simplicity and mathematical tractability.

关键词： Computational modeling Upper bound Probabilistic logic Microeconomics Two dimensional displays Estimation Analytical models

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Power system state estimation via feasible point pursuit: Algorithms and Cramér-Rao bound

arXiv

引用

arXiv 2017年

作者： Wang, Gang Zamzam, Ahmed S. Giannakis, Georgios B. Sidiropoulos, Nicholas D. Digital Tech. Center ECE Dept. U. of Minnesota MplsMN55455 United States State Key Lab. of Intelligent Control and Decision of Complex Systems Beijing Inst. of Tech. Beijing100081 China

Accurately monitoring the system's operating point is central to the reliable and economic operation of an electric power grid. Power system state estimation (PSSE) aims to obtain complete voltage magnitude and angle information at each bus given a number of system variables at selected buses and lines. Power flow analysis is a special case of PSSE, and amounts to solving a set of noise-free power flow equations. Physical laws dictate quadratic relationships between available quantities and unknown voltages, rendering general instances of power flow and PSSE nonconvex and NP-hard. Past approaches are largely based on gradient-type iterative procedures or semidefinite relaxation (SDR). Due to nonconvexity, the solution obtained via gradienttype schemes depends on initialization, while SDR methods do not perform as desired in challenging scenarios. This paper puts forth novel feasible point pursuit (FPP)-based solvers for power flow and PSSE, which iteratively seek feasible solutions for a nonconvex quadratically constrained quadratic programming (QCQP) reformulation of the weighted least-squares (WLS) problem. Relative to the prior art, the developed solvers offer superior performance at the cost of higher complexity. Furthermore, they converge to a stationary point of the WLS problem. As a baseline for comparing different estimators, the Cramér-Rao lower bound (CRLB) is derived for the fundamental PSSE problem in this paper. Judicious numerical tests on several IEEE benchmark systems showcase markedly improved performance of our FPPbased solvers for both power flow and PSSE tasks over popular WLS-based Gauss-Newton iterations and SDR approaches. Copyright © 2017, The Authors. All rights reserved.

关键词： state estimation

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Riemannian Stein variational gradient descent for Bayesian inference

arXiv

引用

arXiv 2017年

作者： Liu, Chang Zhu, Jun Dept. of Comp. Sci. & Tech. TNList Lab Center for Bio-Inspired Computing Research State Key Lab for Intell. Tech. & Systems Tsinghua University Beijing China

We develop Riemannian Stein Variational Gradient Descent (RSVGD), a Bayesian inference method that generalizes Stein Variational Gradient Descent (SVGD) to Riemann manifold. The benefits are two-folds: (i) for inference tasks in Euclidean spaces, RSVGD has the advantage over SVGD of utilizing information geometry, and (ii) for inference tasks on Riemann manifolds, RSVGD brings the unique advantages of SVGD to the Riemannian world. To appropriately transfer to Riemann manifolds, we conceive novel and non-trivial tech.iques for RSVGD, which are required by the intrinsically different characteristics of general Riemann manifolds from Euclidean spaces. We also discover Riemannian Stein's Identity and Riemannian Kernelized Stein Discrepancy. Experimental results show the advantages over SVGD of exploring distribution geometry and the advantages of particle-efficiency, iteration-effectiveness and approximation flexibility over other inference methods on Riemann manifolds. Copyright © 2017, The Authors. All rights reserved.

关键词： Gradient methods

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Solving Large-scale systems of Random Quadratic Equations via Stochastic Truncated Amplitude Flow

Solving Large-scale Systems of Random Quadratic Equations vi...

引用

European Signal Processing Conference

作者： Gang Wang Georgios B. Giannakis Jie Chen Dept. of ECE and Digital Tech. Center Univ. of Minnesota Mpls MN USA State Key Lab of Intelligent Control and Decision of Complex Systems Beijing Institute of Technology Beijing China

This work develops a new iterative algorithm, which is called stochastic truncated amplitude flow (STAF), to recover an unknown signal x ∈ R~n from m "phaseless" quadratic equations of the form ψ_i = |a_i~T x|, 1 ≤ i ≤ m. This problem is also known as phase retrieval, which is NP-hard in general. Building on an amplitude-based nonconvex least-squares formulation, STAF proceeds in two stages: s1) Orthogonality-promoting initialization computed using a stochastic variance reduced gradient algorithm;and, s2) Refinements of the initial point through truncated stochastic gradient-type iterations. Both stages handle a single equation per iteration, therefore lending STAF well to Big Data applications. Specifically for independent Gaussian {a_i}_(i=1)~m vectors, STAF recovers exactly any x exponentially fast when there are about as many equations as unknowns. Finally, numerical tests demonstrate that STAF improves upon its competing alternatives.

关键词： Stochastic processes Signal processing algorithms Runtime Mathematical model Computational complexity Europe Signal processing

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WarpLDA: A cache efficient O(1) algorithm for latent dirichlet allocation

WarpLDA: A cache efficient O(1) algorithm for latent dirichl...

引用

42nd International Conference on Very Large Data Bases, VLDB 2016

作者： Chen, Jianfei Li, Kaiwei Zhu, Jun Chen, Wenguang Dept. of Comp. Sci. and Tech. TNList Lab CBICR Center Tsinghua University China State Key Lab of Intelligent Technology and Systems Beijing100084 China China

Developing efficient and scalable algorithms for Latent Dirichlet Allocation (LDA) is of wide interest for many applications. Previous work has developed an O(1) Metropolis-Hastings (MH) sampling method for each token. However, its performance is far from being optimal due to frequent cache misses caused by random accesses to the parameter matrices. In this paper, we first carefully analyze the memory access behavior of existing algorithms for LDA by cache locality at document level. We then develop WarpLDA, which achieves O(1) time complexity per-token and fits the randomly accessed memory perdocument in the L3 cache. Our empirical results in a wide range of testing conditions demonstrate thatWarpLDA is consistently 5-15x faster than the state-of-the-art MH-based LightLDA, and is faster than the state-of-the-art sparsity aware F+LDA in most settings. Our WarpLDA learns a million topics from 639 millions of documents in only five hours at an unprecedented throughput of 11 billion tokens per second. © 2016 VLDB Endowment.

关键词： Statistics

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Conditional generative moment-matching networks 30

Conditional generative moment-matching networks

引用

30th Annual Conference on Neural Information Processing systems, NIPS 2016

作者： Ren, Yong Li, Jialian Luo, Yucen Zhu, Jun Dept. of Comp. Sci. and Tech. TNList Lab. Tsinghua University Beijing China Center for Bio-Inspired Computing Research State Key Lab. for Intell. Tech. and Systems Tsinghua University Beijing China

Maximum mean discrepancy (MMD) has been successfully applied to learn deep generative models for characterizing a joint distribution of variables via kernel mean embedding. In this paper, we present conditional generative moment-matching networks (CGMMN), which learn a conditional distribution given some input variables based on a conditional maximum mean discrepancy (CMMD) criterion. The learning is performed by stochastic gradient descent with the gradient calculated by back-propagation. We evaluate CGMMN on a wide range of tasks, including predictive modeling, contextual generation, and Bayesian dark knowledge, which distills knowledge from a Bayesian model by learning a relatively small CGMMN student network. Our results demonstrate competitive performance in all the tasks. © 2016 NIPS Foundation - All Rights Reserved.

关键词： Backpropagation

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Kernel Bayesian inference with posterior regularization 30

Kernel Bayesian inference with posterior regularization

引用

30th Annual Conference on Neural Information Processing systems, NIPS 2016

作者： Song, Yang Zhu, Jun Ren, Yong Dept. of Physics Tsinghua University Beijing China Dept. of Comp. Sci. and Tech. TNList Lab. Tsinghua University Beijing China Center for Bio-Inspired Computing Research State Key Lab for Intell. Tech. and Systems Tsinghua University Beijing China

We propose a vector-valued regression problem whose solution is equivalent to the reproducing kernel Hilbert space (RKHS) embedding of the Bayesian posterior distribution. This equivalence provides a new understanding of kernel Bayesian inference. Moreover, the optimization problem induces a new regularization for the posterior embedding estimator, which is faster and has comparable performance to the squared regularization in kernel Bayes' rule. This regularization coincides with a former thresholding approach used in kernel POMDPs whose consistency remains to be established. Our theoretical work solves this open problem and provides consistency analysis in regression settings. Based on our optimizational formulation, we propose a flexible Bayesian posterior regularization framework which for the first time enables us to put regularization at the distribution level. We apply this method to nonparametric state-space filtering tasks with extremely nonlinear dynamics and show performance gains over all other baselines. © 2016 NIPS Foundation - All Rights Reserved.

关键词： Inference engines

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Stochastic gradient geodesic MCMC methods 30

Stochastic gradient geodesic MCMC methods

引用

30th Annual Conference on Neural Information Processing systems, NIPS 2016

作者： Liu, Chang Zhu, Jun Song, Yang Dept. of Comp. Sci. and Tech. TNList Lab. Center for Bio-Inspired Computing Research Beijing China State Key Lab for Intell. Tech. and Systems Tsinghua University Beijing China Dept. of Physics Tsinghua University Beijing China Department of Computer Science Stanford University CA United States

We propose two stochastic gradient MCMC methods for sampling from Bayesian posterior distributions defined on Riemann manifolds with a known geodesic flow, e.g. hyperspheres. Our methods are the first scalable sampling methods on these manifolds, with the aid of stochastic gradients. Novel dynamics are conceived and 2nd-order integrators are developed. By adopting embedding tech.iques and the geodesic integrator, the methods do not require a global coordinate system of the manifold and do not involve inner iterations. Synthetic experiments show the validity of the method, and its application to the challenging inference for spherical topic models indicate practical usability and efficiency. © 2016 NIPS Foundation - All Rights Reserved.

关键词： Stochastic systems

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Conditional Generative Moment-Matching Networks 16

Conditional Generative Moment-Matching Networks

引用

Annual Conference on Neural Information Processing systems

作者： Yong Ren Jialian Li Yucen Luo Jun Zhu Dept. of Comp. Sci. & Tech. TNList LabCenter for Bio-Inspired Computing Research State Key Lab for Intell. Tech. & Systems Tsinghua University Beijing China

ISBN: (纸本)9781510838819

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PSSE Redux: Convex relaxation, decentralized, robust, and dynamic approaches

arXiv

引用

arXiv 2017年

作者： Kekatos, Vassilis Wang, Gang Zhu, Hao Giannakis, Georgios B. Bradley Department of Electrical and Computer Engineering Virginia Tech BlacksburgVA24061 United States Department of Electrical and Computer Engineering University of Minnesota MinneapolisMN55455 United States State Key Lab of Intelligent Control and Decision of Complex Systems Beijing Institute of Technology Beijing100081 China Department of Electrical and Computer Engineering University of Illinois UrbanaIL61801 United States

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