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Dec. 6 | Modeling long-range dependence in mortality: Implications on natural hedging and index-based hedging of longevity
Time:Dec.6 (Tuesday),9:00-10:30Venue:ZoomConference ID:813-2065-1303; Code:679275Speaker:Kenneth Q. Zhou(Assisstant Professor),Arizona State UniversitySummary:Long-range dependence (LRD) in mortality dynamics has been identified and studied in the recent actuarial literature. The non-Markovian feature caused by LRD can raise new research questions and challenges in actuarial pricing and risk management. The first part of this talk discusses a new continuous-time modeling approach that uses a com
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Nov. 30 | Evaluating Promotion Designs in Mobile Payment Networks
Time:Nov.30 (Wednesday),10:00-11:30Venue:Science Building A1214, Tencent Conference ID:583-5094-2312; Code:1214Speaker:Tao Xuezhen, Assistant Professor, Shanghai University of Finance and EconomicsSummary:Financial promotions are widely adopted to establish and maintain market share. We use mobile payments to empirically evaluate the effectiveness of various promotion designs and quantify the basic trade-offs on consumers’ payment preferences. Using a dataset of students’ mobile payment records
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Nov. 29 | Large-scale Detection of Differential Sparsity Structure with FDR Control
Time:Nov.29 (Tuesday),10:00-11:00Venue:Science Building A1514, Tencent Conference ID:992-165-109; Code:1309Speaker:Ren Haojie, Associate Professor, Shanghai Jiaotong UniversitySummary:Two-sample multiple testing has a wide range of applications. Most of the literature considers simultaneous tests of equality of parameters. This talk takes a different perspective and investigates the null hypotheses that the two support sets are equal. This formulation of the testing problem is motivated by the f
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Nov. 25 | Bayesian phylogenetic inference via sequential Monte Carlo approaches
Time:Nov.25 (Friday),14:00-15:00Venue:Science Building A1314, Tencent Conference ID:844-333-509Speaker:Wang Shijia, Associate Professor, Nankai UniversitySummary:Phylogenetic tree reconstruction is a main task in evolutionary biology. Traditional MCMC methods may suffer from the curse of dimensionality and the local-trap problem. Sequential Monte Carlo methods have emerged as alternatives to MCMC methods for phylogenetic reconstruction. Firstly, we introduce a new combinatorial SMC method, with
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Nov. 22 | New Scalable and Adaptive Algorithms for Deep Learning with Unbounded Smooth Landscape
Time:Nov.22 (Tuesday),10:00-11:00Venue:Tencent Conference ID:144-899-652Speaker:Mingrui Liu, Associate Professor,George Mason UniversitySummary:The current analysis of deep learning optimization algorithms typically requires the landscape to be smooth. However, there is a class of deep neural networks which is inherently nonsmooth, with potentially unbounded smoothness parameter. The existing work addressed this issue by gradient clipping, but this algorithm is not scalable in the multi-agent di
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