Di Chen: End-to-End learning for the Deep Multivariate Probit Model

Di Chen, Cornell University
Sep 21, 2018
Title: End-to-End learning for the Deep Multivariate Probit Model
CompSust Open Graduate Seminar (COGS)
www.compsust.ne...
Abstract:
Understanding multi-entity interactions is a central question in many real-world applications. For example, in computational sustainability, it is important to understand the spatial distribution of species and how species interact with each other and their environment, for developing conservation plans. In computer vision, the detections of multiple objects are often correlated because of the shared background and scenario. In natural language processing, a text often has several correlated labels in terms of its topic, emotion, and semantic meaning.
The multivariate probit model (MVP) is a popular classic model for studying binary responses of multiple entities. Nevertheless, the computational challenge of learning the MVP model, given that its likelihood involves integrating over a multi-dimensional constrained space of latent variables, significantly limits its application in practice.
In this talk, I will present a flexible deep generalization of the classic MVP, the Deep Multivariate Probit Model (DMVP), which is an end-to-end learning scheme that uses an efficient parallel sampling process of the multivariate probit model to exploit GPU-boosted deep neural networks. We show that when applied to multi-entity modelling problems, which are natural DMVP applications, DMVP trains faster than classical MVP, by at least an order of magnitude, captures rich correlations among entities, and further improves the joint likelihood of entities compared with several competitive models.
The talk will run about 30 minutes, with an extended 20 minutes question and discussion segment following the talk. I released my code on the bitbucket and you are welcome to use DMVP to explore the interesting multi-entity correlation in your own domain.
Bio:
Di Chen is a second-year Ph.D. student in the Department of Computer Science at Cornell University, advised by Carla P. Gomes. His research includes solving structured prediction, multi-entity modeling and covariate shift using state-of-the-art deep learning techniques.

[@simachewyedemie]

the formula is not clear or what it does mean and what indicates , the other is how can we test the MVP model efficiency and how can we analyze ?

[@mo123296]

Dear bro. ; How are you doing? I am a student in Ethiopia, now I want to use MVProbit model. So, can you tell me the assumptions for the model and how to calculate the marginal effect? Thank you