Access to Higher Education and Inequality: The Chinese Experiment We apply a semi-parametric latent variable model to estimate selection and sorting effects on the evolution of private returns to schooling for college graduates during China’s reform between 1988 and 2002. We find that there were substantial sorting gains under the traditional system, but they have decreased drastically and beco...

In the medical, behavioral, social, and psychological sciences, latent variables represent unobservable traits that are measured by multiple observed variables. Latent variable models (LVMs) are useful tools for assessing the interrelationships among latent and observed variables. Due to their wide applications, LVMs have attracted significant attention from various fields. However, the majorit...

If there is one crucial problem with the application of so-called psychoJogical test theories in education (or in any other context). it is that both test deveJopers and test usersjust don 't know what they are doing. Let me give you two examples: First. we have been testing intelligence for three quarters ofa century now, but there are virtually no test psychologists who can tell what intellig...

Discriminative latent variable models (LVM) are frequently applied to various visual recognition tasks. In these systems the latent (hidden) variables provide a formalism for modeling structured variation of visual features. Conventionally, latent variables are defined on the variation of the foreground (positive) class. In this work we augment LVMs to include negative latent variables correspo...

We consider the problem of inference in a probabilistic model for transient populations where we wish to learn about arrivals, departures, and population size over all time, but the only available data are periodic counts of the population size at specific observation times. The underlying model arises in queueing theory (as an Mt/G/∞ queue) and also in ecological models for short-lived animals...

In this submission for MIREX 2014 we utilize an efficient latent variable model for multiple-F0 estimation and note tracking, which uses a variable-Q transform as a timefrequency representation. In contrast to the constant-Q transform of the 2013 submission, the variable-Q transform is able to provide a better temporal resolution in low frequencies with the same frequency resolution. The model ...

Generative modeling of high-dimensional data is a key problem in machine learning. Successful approaches include latent variable models and autoregressive models. The complementary strengths of these approaches, to model global and local image statistics respectively, suggest hybrid models combining the strengths of both models. Our contribution is to train such hybrid models using an auxiliary...

In many statistical problems, a more coarse-grained model may be suitable for population-level behaviour, whereas a more detailed model is appropriate for accurate modelling of individual behaviour. This raises the question of how to integrate both types of models. Methods such as posterior regularization follow the idea of generalized moment matching, in that they allow matching expectations b...

This rejoinder discusses the general comments on how to use BSEM wisely and how to get more people better trained in using Bayesian methods. Responses to specific comments cover how to handle sign switching, nonconvergence and non-identification, and prior choices in latent variable models. Two new applications are included. The first one revisits the Kaplan science model by considering priors ...

Numerical methods in higher dimensions using tensor factorizations I this talk I will collect recent advances in the solution of high-dimensional problems in different application areas: chemistry, biology, mathematics. The language of low-rank factorization gives a unified view on different algorithms for the solution of seemingly diverse and unconnected problems. Typical applications include ...