Seminars are held on Thursdays at 4:00PM. Please note the location for each event in the schedule below, which will either be Cullimore 611 (CULM 611) or the Campus Center. For questions about the seminar schedule, please contact Antai Wang.
|Date||Location||Speaker, Affiliation, and Title||Host|
|September 28||CTR 215||Wei Sun, Department of Management Science, University of Miami
Personalized Advertising and Ad Clustering via Sparse Tensor Methods
Tensor as a multi-dimensional generalization of matrix has received increasing attention in industry due to its success in personalized recommendation systems. Traditional recommendation systems are mainly based on the user-item matrix, whose entry denotes each user's preference for a particular item. To incorporate additional information into the analysis, such as the temporal behavior of users, we encounter a user-item-time tensor. Existing tensor decomposition methods are mostly established in the non-sparse regime where the decomposition components include all features. In online advertising, the ad-click tensor is usually sparse due to the rarity of ad clicks.
In this talk, I will discuss a new sparse tensor decomposition method that incorporates the sparsity of each latent component to the CP tensor decomposition. In theory, in spite of the non-convexity of the optimization problem, it is proven that an alternating updating algorithm attains an estimator whose rate of convergence significantly improves those shown in non-sparse decomposition methods. The potential business impact of our method is demonstrated via an application of click-through rate prediction for personalized advertising.
In the second part of the talk, I will discuss an extension of the proposed sparse tensor decomposition to handle multiple sources of tensor data. In online advertising, the users’ click behavior on different ads from multiple devices forms a user-ad-device tensor, and the ad characteristics data forms an ad-feature matrix. We propose a unified learning framework to extract latent features embedded in both tensor data and matrix data. We conduct cluster analysis of advertisements based on the extracted latent features and provide meaningful insights in linking different ad industries.
Speaker Introduction: Will Wei Sun is currently an assistant professor of Management Science at University of Miami School of Business Administration. Before that, he was a research scientist in the advertising science team at Yahoo labs. He obtained his PhD in Statistics from Purdue University in 2015. Dr. Sun’s research focuses on machine learning, with applications in computational advertising, personalized recommendation system, and Neuroimaging analysis.
|CULM 611||Jing Qiu, Department of Applied Economics and Statistics, University of Delaware
FDR Control of the High Dimensional TOST Tests
High dimensional equivalence testing is a very important but seldom studied problem. When researchers look for equivalently expressed genes, the common practice is to conduct differential tests and treat genes that are not differentially expressed as equivalently expressed genes. This is statistically not valid because it does not control the type I error appropriately. An appropriate way is to conduct equivalence tests. A well-known equivalence test is two one-sided tests (TOST). The existing FDR controlling methods are over-conservative for equivalence tests. We investigate the performance of existing FDR controlling methods and propose three new methods to control the FDR for equivalence test.
|October 12||CTR 230||Xin Yuan, Bell Labs
|November 9||CULM 611||Yaqun Wang, Department of Biostatistics, Rutgers University
|November 16||CTR 230||Weijie Su, Department of Statistics, University of Pennsylvania
|December 7||CULM 611||Annie Qu, Department of Mathematical Sciences, University of Illinois at Urbana Champaign
Updated: October 5, 2017