Colloquium/Seminar

YearMonth
2017 Jan   Feb   Mar   Apr   May   Jun   Jul   Aug   Oct   Nov   Dec  
2016 Jan   Feb   Mar   Apr   May   Jun   Jul   Aug   Oct   Nov   Dec  
2015 Jan   Feb   Mar   Apr   May   Jun   Aug   Sep   Oct   Nov   Dec  
2014 Jan   Feb   Mar   Apr   May   Jun   Jul   Aug   Sep   Oct   Nov   Dec  
2013 Jan   Feb   Mar   Apr   May   Jun   Aug   Sep   Nov   Dec  
2012 Jan   Feb   Apr   May   Jun   Jul   Aug   Sep   Nov   Dec  
2011 Jan   Feb   Mar   Apr   May   Jun   Jul   Aug   Sep   Oct   Nov   Dec  
2010 Jan   Feb   Mar   Apr   May   Jun   Sep   Oct   Nov   Dec  
2009 Jan   Feb   Mar   Apr   May   Jun   Jul   Aug   Sep   Oct   Nov   Dec  
2008 Jan   Feb   Mar   Apr   May   Jun   Jul   Aug   Sep   Oct   Nov   Dec  
2007 Jan   Feb   Mar   Apr   May   Jun   Jul   Aug   Sep   Oct   Nov   Dec  
2006 Jan   Feb   Mar   Apr   May   Jun   Jul   Sep   Oct   Nov   Dec  
2005 Jan   Feb   Mar   Apr   May   Jun   Jul   Aug   Sep   Oct   Nov   Dec  
2004 Jan   Feb   Mar   Apr   May   Aug   Sep   Oct   Nov   Dec  

Event(s) on November 2015


  • Tuesday, 3rd November, 2015

    Title: Linear Algebra Computation in Data Science
    Speaker: Prof. Zhaojun BAI, Department of Computer Science, University of California, USA
    Time/Place: 11:30  -  12:30
    FSC1217, Fong Shu Chuen Library, HSH Campus, Hong Kong Baptist University
    Abstract: Linear algebra computation is one of the seven computational giants for massive data analysis, along with basic statistics, generalized N-body problems, graph-theoretical computations, optimization, integration and alginment problems [National Research Council, Frontiers in Massive Data Analysis, The National Academies Press, 2013]. In this talk, we will discuss a number of linear algebra problems arising from data analysis techniques. In particular, we will discuss Rayleigh-Quotient (RQ) and RQ-type optimizations in robust classification to handle uncertainty and in constrained partitioning to incorporate a prior information. We will show that many of these RQ and RQ-type optimizations can be reformulated as linear or nonlinear eigenvalue problems. The use of modern solvers for these eigenvalue problems will also be discussed.


  • Wednesday, 11th November, 2015

    Title: On an inverse problem in photolithography
    Speaker: Prof. Luca RONDI, Department of Mathematics and Geosciences, University of Trieste, Italy
    Time/Place: 16:30  -  17:30
    SCT909, Cha Chi-ming Science Tower, HSH Campus, Hong Kong Baptist University
    Abstract: The inverse photolithography problem is a key step in the production of integrated circuits. In this talk I present a regularization and computation strategy for this inverse problem. The method is based on a variational approach whose key feature is a regularization procedure for a suitable thresholding operation. The validity of the method is shown by a convergence analysis and by numerical experiments. This is a joint work with Fadil Santosa (University of Minnesota) and Zhu Wang (University of South Carolina).


  • Wednesday, 18th November, 2015

    Title: Learning methods for safely using unlabelled data
    Speaker: Dr. Yufeng LI, National Key Laboratory for Novel Software Technology, Nanjing University, China
    Time/Place: 11:00  -  12:00
    FSC1217, Fong Shu Chuen Library, HSH Campus, Hong Kong Baptist University
    Abstract: When the amount of labelled data is limited, it is usually expected that learning methods exploiting additional unlabelled data will help improve learning performance. In many situations, however, it is reported that learning methods using unlabelled data may even decrease the learning performance. This phenomenon affects the deployment of unlabelled data learning methods in real-world situations. It is thus desirable to develop “safe” unlabelled data learning methods that often improve performance, while in the worst cases do not decrease the learning performance. In this talk, I introduce our recent progresses in this direction. By considering the performance degeneration may be caused by not-good-enough solution in large-scale optimization, we present a scalable method which is able to optimize a tight convex upper bound of the objective in an efficient manner. By considering that the performance degeneration may be caused by the uncertainty of model selection, we present a learning method which optimizes the worst-case accuracy improvement and hence avoids the harm of uncertain model selection. Furthermore, by considering that real-world applications require variants of performance measures and the performance degeneration may be caused by the difficulty in optimizing complicate performance measures, we present to optimize the worst-case performance gain under complicate performance measures and show that when the performance measure is AUC, F1 and Top-k precision, the minimax convex relaxation of the objective could be solved globally and efficiently. Experiments validate the effectiveness of the proposal method.


  • Friday, 20th November, 2015

    Title: Numerical methods for uncertainty quantification for multiphase flow in porous media
    Speaker: Prof. Rolf JELTSCH, ETH, Zurich, Switzerland
    Time/Place: 11:30  -  12:30
    FSC1217, Fong Shu Chuen Library, HSH Campus, Hong Kong Baptist University
    Abstract: Multiphase flow models are used to understand the extraction of oil or gas in porous media or store Carbon Dioxide in empty reservoirs. Mathematically the models consist of a system of nonlinear partial differential equations. The phase pressure is modeled using an elliptic equation while the phase saturation is a hyperbolic system if capillary pressure is neglected. Many physical input variables, e.g. rock permeability, relative permeability are determined by measurement processes and hence are prone to uncertainty. Hence one needs to model uncertainty to be able to compute the effects. We report on a recently started research project in Brazil and review the current state of the art.


  • Tuesday, 24th November, 2015

    Title: SDNA: Stochastic Dual Newton Ascent for Empirical Risk Minimization
    Speaker: Dr. Zheng QU, Department of Mathematics, The University of Hong Kong, Hong Kong
    Time/Place: 11:00  -  12:00
    FSC1217, Fong Shu Chuen Library, HSH Campus, Hong Kong Baptist University
    Abstract: We propose and analyze a randomized block coordinate descent methods with overlapping blocks. In each iteration our method picks a random subset of the coordinates, following an arbitrary probability law, and maximizes, exactly, the objective restricted to the random subspace spanned by the coordinates. Equivalently, this can be seen as the solution of a proximal subproblem involving a random principal submatrix of the Hessian of the quadratic function. Hence, our method is capable of utilizing all curvature information available in the random subspace in which it operates, which leads to striking striking improvements in both theory and practice, sometimes by orders of magnitude. Note that this is very different from the update strategy of parallel/minibatch coordinate descent methods. Indeed, while these methods also update a random subset of variables in each iteration, they instead only utilize curvature information present in the diagonal of the Hessian. We then instantiate this method to derive a new algorithm-Stochastic Dual Newton Ascent (SDNA)-for solving regularized empirical risk minimization problem with smooth loss functions and strongly convex regularizer.


  • Saturday, 28th November, 2015

    Title: 香港浸會大學數學系四十五週年校友講座 - 數學教師成【長】之路
    Speaker: 黃廣威校友 黃志揚校友, Hong Kong
    Time/Place: 14:30  -  16:30
    RRS905, Sir Run Run Shaw Building, HSH Campus, Hong Kong Baptist University


  • Monday, 30th November, 2015

    Title: HKBU MATH 45th Anniversary Distinguished Lecture - Dirichlet Forms and Applications
    Speaker: Prof. Zhi-Ming Ma, Institute of Applied Mathematics, Chinese Academy of Sciences, China
    Time/Place: 16:30  -  17:30 (Preceded by Reception at 4:00pm)
    WLB103, The Wing Lung Bank Building for Business Studies, Shaw Campus, Hong Kong Baptist University
    Abstract: The theory of Dirichlet forms is a powerful mathematical framework which connects deterministic potential theory and stochastic analysis. In this talk I shall briefly introduce the notion of Dirichlet forms and review some of its applications. I shall also present some recent development and results in this research direction.