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Ying Shan - IEEE Xplore Author Profile

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Author details

Ying Shan

Publications

60

Citations

2,234

Publications by Year

20212025

Co-Authors:

Hao AiYuang AiYunpeng BaiLong BaoGuanyu Cai

Show All Co-Authors (228)

Ying Shan

Affiliation

Tencent AI Lab, China

Publication Topics

Self-supervised Learning,
Latent Space,
Multilayer Perceptron,
Diffusion Model,
Training Set,
View Synthesis,
3D Reconstruction,
Generative Adversarial Networks,
Image Features,
Language Model,
Triplet Loss,
Video Frames

Biography

Ying Shan (Senior Member, IEEE) is a distinguished scientist with Tencent, the director of the ARC Lab with Tencent PCG, and the director of the Visual Computing Center with Tencent AI Lab. Before joining Tencent, he worked with Microsoft Research as a post-doc researcher, SRI International (Sarnoff Subsidiary) as a Senior MTS, and Microsoft Bing Ads as a Principal Scientist Manager. He has published more than 100 papers in top conferences and journals in the areas of computer vision, machine learning, and data mining, served as ACs of CVPR and senior PC of KDD.(Based on document published on 14 February 2024).

Publications

60

Citations

2,234

Publications by Year

20212025

Co-Authors:

Hao Ai
Yuang Ai
Yunpeng Bai
Long Bao
Guanyu Cai

Show All Co-Authors (228)

Author's Published Works

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Showing 1-25 of 32 results

Conferences (20)

Journals (12)

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Stephen J. Wright(16)
S.J. Wright(7)
Stephen Wright(5)
Robert D. Nowak(5)
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University of Wisconsin, Madison, USA(5)
Department of Chemical and Biological Engineering, University of Wisconsin, Madison, WI, USA(4)
Faculty of Computer Sciences, University of Wisconsin, Madison, WI, USA(3)
Computer Sciences Department, University of Wisconsin, Madison, WI, USA(3)
Faculty of Chemical and Biological Engineering, University of Wisconsin, Madison, WI, USA(3)
IEEE Transactions on Audio, Speech, and Language Processing(2)
IEEE Transactions on Signal Processing(2)
Proceedings of the IEEE(2)
2006 American Control Conference(1)
2007 IEEE/SP 14th Workshop on Statistical Signal Processing(1)
IEEE(32)
Albuquerque, NM, USA(1)
Atlanta, GA, USA(1)
Baltimore, MD, USA(1)
Brighton, UK(1)
Brussels, Belgium(1)
Optimization Problem(9)
Model Predictive Control(8)
Objective Function(7)
Optimal Control(7)
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PMU Placement for Line Outage Identification via Multinomial Logistic Regression

Taedong Kim;Stephen J. Wright

IEEE Transactions on Smart Grid

Year: 2018 | Volume: 9, Issue: 1 | Journal Article |

Cited by: Papers (44)

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We consider the problem of identifying a single line outage in a power grid by using data from phasor measurement units (PMUs). When a line outage occurs, the voltage phasor of each bus node changes in response to the change in network topology. Each individual line outage has a distinctive signature, and a multinomial logistic regression classifier can be trained to distinguish between these sign...Show More

PMU Placement for Line Outage Identification via Multinomial Logistic Regression

Taedong Kim;Stephen J. Wright

IEEE Transactions on Smart Grid

Year: 2018 | Volume: 9, Issue: 1 | Journal Article |

Analyzing Vulnerability of Power Systems with Continuous Optimization Formulations

Taedong Kim;Stephen J. Wright;Daniel Bienstock;Sean Harnett

IEEE Transactions on Network Science and Engineering

Year: 2016 | Volume: 3, Issue: 3 | Journal Article |

Cited by: Papers (43)

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Potential vulnerabilities in a power grid can be exposed by identifying those transmission lines on which attacks (in the form of interference with their transmission capabilities) causes maximum disruption to the grid. In this study, we model the grid by (nonlinear) AC power flow equations, and assume that attacks take the form of increased impedance along transmission lines. We quantify disrupti...Show More

Analyzing Vulnerability of Power Systems with Continuous Optimization Formulations

Taedong Kim;Stephen J. Wright;Daniel Bienstock;Sean Harnett

IEEE Transactions on Network Science and Engineering

Year: 2016 | Volume: 3, Issue: 3 | Journal Article |

Forward–Backward Greedy Algorithms for Atomic Norm Regularization

Nikhil Rao;Parikshit Shah;Stephen Wright

IEEE Transactions on Signal Processing

Year: 2015 | Volume: 63, Issue: 21 | Journal Article |

Cited by: Papers (33)

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In many signal processing applications, the aim is to reconstruct a signal that has a simple representation with respect to a certain basis or frame. Fundamental elements of the basis known as “atoms” allow us to define “atomic norms” that can be used to formulate convex regularizations for the reconstruction problem. Efficient algorithms are available to solve these formulations in certain specia...Show More

Forward–Backward Greedy Algorithms for Atomic Norm Regularization

Nikhil Rao;Parikshit Shah;Stephen Wright

IEEE Transactions on Signal Processing

Year: 2015 | Volume: 63, Issue: 21 | Journal Article |

Forward — Backward greedy algorithms for signal demixing

Nikhil Rao;Parikshit Shah;Stephen Wright

2014 48th Asilomar Conference on Signals, Systems and Computers

Year: 2014 | Conference Paper |

Cited by: Papers (2)

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Signal demixing arises in many applications. Common among these are the separation of sparse and low rank components in image and video processing, sparse and group sparse models in multitask learning and spikes and sinusoids in source separation problems. For specific problems of interest, many methods exist to perform recovery, but an approach that generalizes to arbitrary notions of simplicity ...Show More

Forward — Backward greedy algorithms for signal demixing

Nikhil Rao;Parikshit Shah;Stephen Wright

2014 48th Asilomar Conference on Signals, Systems and Computers

Year: 2014 | Conference Paper |

Online algorithms for factorization-based structure from motion

Ryan Kennedy;Laura Balzano;Stephen J. Wright;Camillo J. Taylor

IEEE Winter Conference on Applications of Computer Vision

Year: 2014 | Conference Paper |

Cited by: Papers (3)

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We present a family of online algorithms for real-time factorization-based structure from motion, leveraging a relationship between the incremental singular value decomposition and recent work in online matrix completion. Our methods are orders of magnitude faster than previous state of the art, can handle missing data and a variable number of feature points, and are robust to noise and sparse out...Show More

Online algorithms for factorization-based structure from motion

Ryan Kennedy;Laura Balzano;Stephen J. Wright;Camillo J. Taylor

IEEE Winter Conference on Applications of Computer Vision

Year: 2014 | Conference Paper |

On GROUSE and incremental SVD

Laura Balzano;Stephen J. Wright

2013 5th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)

Year: 2013 | Conference Paper |

Cited by: Papers (31)

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GROUSE (Grassmannian Rank-One Update Subspace Estimation) [1] is an incremental algorithm for identifying a subspace of ℝn from a sequence of vectors in this subspace, where only a subset of components of each vector is revealed at each iteration. Recent analysis [2] has shown that GROUSE converges locally at an expected linear rate, under certain assumptions. GROUSE has a similar flavor to the in...Show More

On GROUSE and incremental SVD

Laura Balzano;Stephen J. Wright

2013 5th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)

Year: 2013 | Conference Paper |

A greedy forward-backward algorithm for atomic norm constrained minimization

Nikhil Rao;Parikshit Shah;Stephen Wright;Robert Nowak

2013 IEEE International Conference on Acoustics, Speech and Signal Processing

Year: 2013 | Conference Paper |

Cited by: Papers (14)

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In many applications in signal and image processing, communications, and system identification, one aims to recover a signal that has a simple representation in a given basis or frame. Key devices for obtaining such representations are objects called atoms, and functions called atomic norms. These concepts unify the idea of simple representations across several known applications, and motivate ext...Show More

A greedy forward-backward algorithm for atomic norm constrained minimization

Nikhil Rao;Parikshit Shah;Stephen Wright;Robert Nowak

2013 IEEE International Conference on Acoustics, Speech and Signal Processing

Year: 2013 | Conference Paper |

Optimization Algorithms and Applications for Speech and Language Processing

Stephen J. Wright;Dimitri Kanevsky;Li Deng;Xiaodong He;Georg Heigold;Haizhou Li

IEEE Transactions on Audio, Speech, and Language Processing

Year: 2013 | Volume: 21, Issue: 11 | Journal Article |

Cited by: Papers (19)

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Optimization techniques have been used for many years in the formulation and solution of computational problems arising in speech and language processing. Such techniques are found in the Baum-Welch, extended Baum-Welch (EBW), Rprop, and GIS algorithms, for example. Additionally, the use of regularization terms has been seen in other applications of sparse optimization. This paper outlines a range...Show More

Optimization Algorithms and Applications for Speech and Language Processing

Stephen J. Wright;Dimitri Kanevsky;Li Deng;Xiaodong He;Georg Heigold;Haizhou Li

IEEE Transactions on Audio, Speech, and Language Processing

Year: 2013 | Volume: 21, Issue: 11 | Journal Article |

Overview of large scale optimization for discriminative training in speech recognition

Dimitri Kanevsky;Georg Heigold;Stephen Wright;Hermann Ney

2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Year: 2012 | Conference Paper |

Cited by: Papers (1)

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Over the past few decades, a variety of specialized approaches have been proposed to solve large problems in speech recognition. Conventional optimization techniques have not been widely applied, because the problems do not readily admit an objective for evaluating a given set of parameters and because of the large number of parameters. This situation is changing, due to recent developments in alg...Show More

Overview of large scale optimization for discriminative training in speech recognition

Dimitri Kanevsky;Georg Heigold;Stephen Wright;Hermann Ney

2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Year: 2012 | Conference Paper |

Inherently robust suboptimal nonlinear MPC: Theory and application

Gabriele Pannocchia;James B. Rawlings;Stephen J. Wright

2011 50th IEEE Conference on Decision and Control and European Control Conference

Year: 2011 | Conference Paper |

Cited by: Papers (13)

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We discuss inherent robust stability properties of discrete-time nonlinear systems controlled by Model Predictive Control (MPC) algorithms that do not necessarily attain the global minimum of the optimization problem solved at each sample time. For these implementable suboptimal MPC algorithms, we prove nominal exponential stability of the origin of the closed-loop system. The stability property i...Show More

Inherently robust suboptimal nonlinear MPC: Theory and application

Gabriele Pannocchia;James B. Rawlings;Stephen J. Wright

2011 50th IEEE Conference on Decision and Control and European Control Conference

Year: 2011 | Conference Paper |

Convex approaches to model wavelet sparsity patterns

Nikhil S. Rao;Robert D. Nowak;Stephen J. Wright;Nick G. Kingsbury

2011 18th IEEE International Conference on Image Processing

Year: 2011 | Conference Paper |

Cited by: Papers (33)

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Statistical dependencies among wavelet coefficients are commonly represented by graphical models such as hidden Markov trees (HMTs). However, in linear inverse problems such as deconvolution, tomography, and compressed sensing, the presence of a sensing or observation matrix produces a linear mixing of the simple Markovian dependency structure. This leads to reconstruction problems that are non-co...Show More

Convex approaches to model wavelet sparsity patterns

Nikhil S. Rao;Robert D. Nowak;Stephen J. Wright;Nick G. Kingsbury

2011 18th IEEE International Conference on Image Processing

Year: 2011 | Conference Paper |

Hierarchical cooperative distributed model predictive control

Brett T. Stewart;James B. Rawlings;Stephen J. Wright

Proceedings of the 2010 American Control Conference

Year: 2010 | Conference Paper |

Cited by: Papers (16) | Patents (1)

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Cooperative distributed model predictive control has recently been shown to provide stabilizing feedback for plants composed of any finite number of dynamically coupled subsystems. No assumption is made about the strength of the coupling between the subsystems. This control scheme provides optimal feedback in the limit of infinite optimization iterates, can be terminated before convergence of the ...Show More

Hierarchical cooperative distributed model predictive control

Brett T. Stewart;James B. Rawlings;Stephen J. Wright

Proceedings of the 2010 American Control Conference

Year: 2010 | Conference Paper |

Computational Methods for Sparse Solution of Linear Inverse Problems

Joel A. Tropp;Stephen J. Wright

Proceedings of the IEEE

Year: 2010 | Volume: 98, Issue: 6 | Journal Article |

Cited by: Papers (758) | Patents (8)

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The goal of the sparse approximation problem is to approximate a target signal using a linear combination of a few elementary signals drawn from a fixed collection. This paper surveys the major practical algorithms for sparse approximation. Specific attention is paid to computational issues, to the circumstances in which individual methods tend to perform well, and to the theoretical guarantees av...Show More

Computational Methods for Sparse Solution of Linear Inverse Problems

Joel A. Tropp;Stephen J. Wright

Proceedings of the IEEE

Year: 2010 | Volume: 98, Issue: 6 | Journal Article |

Sparse Reconstruction by Separable Approximation

Stephen J. Wright;Robert D. Nowak;MÁrio A. T. Figueiredo

IEEE Transactions on Signal Processing

Year: 2009 | Volume: 57, Issue: 7 | Journal Article |

Cited by: Papers (1262) | Patents (4)

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Finding sparse approximate solutions to large underdetermined linear systems of equations is a common problem in signal/image processing and statistics. Basis pursuit, the least absolute shrinkage and selection operator (LASSO), wavelet-based deconvolution and reconstruction, and compressed sensing (CS) are a few well-known areas in which problems of this type appear. One standard approach is to m...Show More

Sparse Reconstruction by Separable Approximation

Stephen J. Wright;Robert D. Nowak;MÁrio A. T. Figueiredo

IEEE Transactions on Signal Processing

Year: 2009 | Volume: 57, Issue: 7 | Journal Article |

Sparse reconstruction by separable approximation

Stephen J. Wright;Robert D. Nowak;Mario A. T. Figueiredo

2008 IEEE International Conference on Acoustics, Speech and Signal Processing

Year: 2008 | Conference Paper |

Cited by: Papers (73) | Patents (1)

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Finding sparse approximate solutions to large underdetermined linear systems of equations is a common problem in signal/image processing and statistics. Basis pursuit, the least absolute shrinkage and selection operator (LASSO), wavelet-based deconvolution and reconstruction, and compressed sensing (CS) are a few well-known areas in which problems of this type appear. One standard approach is to m...Show More

Sparse reconstruction by separable approximation

Stephen J. Wright;Robert D. Nowak;Mario A. T. Figueiredo

2008 IEEE International Conference on Acoustics, Speech and Signal Processing

Year: 2008 | Conference Paper |

Power Awareness in Network Design and Routing

J. Chabarek;J. Sommers;P. Barford;C. Estan;D. Tsiang;S. Wright

IEEE INFOCOM 2008 - The 27th Conference on Computer Communications

Year: 2008 | Conference Paper |

Cited by: Papers (490) | Patents (6)

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Exponential bandwidth scaling has been a fundamental driver of the growth and popularity of the Internet. However, increases in bandwidth have been accompanied by increases in power consumption, and despite sustained system design efforts to address power demand, significant technological challenges remain that threaten to slow future bandwidth growth. In this paper we describe the power and assoc...Show More

Power Awareness in Network Design and Routing

J. Chabarek;J. Sommers;P. Barford;C. Estan;D. Tsiang;S. Wright

IEEE INFOCOM 2008 - The 27th Conference on Computer Communications

Year: 2008 | Conference Paper |

Distributed MPC Strategies With Application to Power System Automatic Generation Control

Aswin N. Venkat;Ian A. Hiskens;James B. Rawlings;Stephen J. Wright

IEEE Transactions on Control Systems Technology

Year: 2008 | Volume: 16, Issue: 6 | Journal Article |

Cited by: Papers (661)

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A distributed model predictive control (MPC) framework, suitable for controlling large-scale networked systems such as power systems, is presented. The overall system is decomposed into subsystems, each with its own MPC controller. These subsystem-based MPCs work iteratively and cooperatively towards satisfying systemwide control objectives. If available computational time allows convergence, the ...Show More

Distributed MPC Strategies With Application to Power System Automatic Generation Control

Aswin N. Venkat;Ian A. Hiskens;James B. Rawlings;Stephen J. Wright

IEEE Transactions on Control Systems Technology

Year: 2008 | Volume: 16, Issue: 6 | Journal Article |

Gradient Projection for Sparse Reconstruction: Application to Compressed Sensing and Other Inverse Problems

MÁrio A. T. Figueiredo;Robert D. Nowak;Stephen J. Wright

IEEE Journal of Selected Topics in Signal Processing

Year: 2007 | Volume: 1, Issue: 4 | Journal Article |

Cited by: Papers (2573) | Patents (41)

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Many problems in signal processing and statistical inference involve finding sparse solutions to under-determined, or ill-conditioned, linear systems of equations. A standard approach consists in minimizing an objective function which includes a quadratic (squared ) error term combined with a sparseness-inducing regularization term. Basis pursuit, the least absolute shrinkage and selection operato...Show More

Gradient Projection for Sparse Reconstruction: Application to Compressed Sensing and Other Inverse Problems

MÁrio A. T. Figueiredo;Robert D. Nowak;Stephen J. Wright

IEEE Journal of Selected Topics in Signal Processing

Year: 2007 | Volume: 1, Issue: 4 | Journal Article |

Toeplitz-Structured Compressed Sensing Matrices

Waheed U. Bajwa;Jarvis D. Haupt;Gil M. Raz;Stephen J. Wright;Robert D. Nowak

2007 IEEE/SP 14th Workshop on Statistical Signal Processing

Year: 2007 | Conference Paper |

Cited by: Papers (190) | Patents (3)

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The problem of recovering a sparse signal x Rn from a relatively small number of its observations of the form y = Ax Rk, where A is a known matrix and k « n, has recently received a lot of attention under the rubric of compressed sensing (CS) and has applications in many areas of signal processing such as data cmpression, image processing, dimensionality reduction, etc. Recent work has established...Show More

Toeplitz-Structured Compressed Sensing Matrices

Waheed U. Bajwa;Jarvis D. Haupt;Gil M. Raz;Stephen J. Wright;Robert D. Nowak

2007 IEEE/SP 14th Workshop on Statistical Signal Processing

Year: 2007 | Conference Paper |

Distributed Output Feedback MPC for Power System Control

Aswin N. Venkat;Ian A. Hiskens;James B. Rawlings;Stephen J. Wright

Proceedings of the 45th IEEE Conference on Decision and Control

Year: 2006 | Conference Paper |

Cited by: Papers (40) | Patents (1)

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In this paper, a distributed output feedback model predictive control (MPC) framework with guaranteed nominal stability and performance properties is described. Distributed state estimation strategies are developed for supporting distributed output feedback MPC of large-scale systems, such as power systems. It is shown that under certain (easily verifiable) conditions, local measurements are suffi...Show More

Distributed Output Feedback MPC for Power System Control

Aswin N. Venkat;Ian A. Hiskens;James B. Rawlings;Stephen J. Wright

Proceedings of the 45th IEEE Conference on Decision and Control

Year: 2006 | Conference Paper |

Implementable distributed model predictive control with guaranteed performance properties

A.N. Venkat;J.B. Rawlings;S.J. Wright

2006 American Control Conference

Year: 2006 | Conference Paper |

Cited by: Papers (14)

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This article describes an implementable distributed MPC framework with guaranteed nominal stability and performance properties. The proposed distributed MPC framework consists of three main components (i) distributed estimator (ii) centralized/distributed target calculation (iii) distributed regulator. State estimation for distributed MPC is addressed using the well established Kalman filtering fr...Show More

Implementable distributed model predictive control with guaranteed performance properties

A.N. Venkat;J.B. Rawlings;S.J. Wright

2006 American Control Conference

Year: 2006 | Conference Paper |

Approximating StreamingWindow Joins Under CPU Limitations

A. Ayad;J. Naughton;S. Wright;U. Srivastava

22nd International Conference on Data Engineering (ICDE'06)

Year: 2006 | Conference Paper |

Cited by: Papers (8)

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Data streaming systems face the possibility of having to shed load in the case of CPU or memory resource limitations. We study the CPU limited scenario in detail. First, we propose a new model for the CPU cost. Then we formally state the problem of shedding load for the goal of obtaining the maximum possible subset of the complete answer, and propose an online strategy for semantic load shedding. ...Show More

Approximating StreamingWindow Joins Under CPU Limitations

A. Ayad;J. Naughton;S. Wright;U. Srivastava

22nd International Conference on Data Engineering (ICDE'06)

Year: 2006 | Conference Paper |

Stability and optimality of distributed model predictive control

A.N. Venkat;J.B. Rawlings;S.J. Wright

Proceedings of the 44th IEEE Conference on Decision and Control

Year: 2005 | Conference Paper |

Cited by: Papers (149) | Patents (1)

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This article extends existing concepts in linear model predictive control (MPC) to a unified, theoretical framework for distributed MPC with guaranteed nominal stability and performance properties. Centralized MPC is largely viewed as impractical, inflexible and unsuitable for control of large, networked systems. Incorporation of the proposed distributed regulator provides a means of achieving opt...Show More

Stability and optimality of distributed model predictive control

A.N. Venkat;J.B. Rawlings;S.J. Wright

Proceedings of the 44th IEEE Conference on Decision and Control

Year: 2005 | Conference Paper |

Minimizing delivery cost in scalable streaming content distribution systems

J.M. Almeida;D.L. Eager;M.K. Vernon;S.J. Wright

IEEE Transactions on Multimedia

Year: 2004 | Volume: 6, Issue: 2 | Journal Article |

Cited by: Papers (48)

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Recent scalable multicast streaming protocols for on-demand delivery of media content offer the promise of greatly reduced server and network bandwidth. However, a key unresolved issue is how to design scalable content distribution systems that place replica servers closer to various client populations and route client requests and response streams so as to minimize the total server and network de...Show More

Minimizing delivery cost in scalable streaming content distribution systems

J.M. Almeida;D.L. Eager;M.K. Vernon;S.J. Wright

IEEE Transactions on Multimedia

Year: 2004 | Volume: 6, Issue: 2 | Journal Article |

An approach to optimizing adaptive parabolic PDE solvers for the Grid

V. Adve;J. Browne;B. Ensink;J. Rice;P. Teller;M. Vernon;S. Wright

Proceedings International Parallel and Distributed Processing Symposium

Year: 2003 | Conference Paper |

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The method of lines is a widely used algorithm for solving parabolic partial differential equations that could benefit greatly from implementation on Grid computing environments. This paper outlines the issues involved in executing method-of-lines codes on a Grid and in developing model-driven adaptive control strategies for these codes. We have developed a parameterizable benchmark called MOL tha...Show More

An approach to optimizing adaptive parabolic PDE solvers for the Grid

V. Adve;J. Browne;B. Ensink;J. Rice;P. Teller;M. Vernon;S. Wright

Proceedings International Parallel and Distributed Processing Symposium

Year: 2003 | Conference Paper |

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