We propose probabilistic latent variable models for multi-view anomaly detection, which is the task of finding instances that have inconsistent views given multi-view data. With the proposed model, all views of a non-anomalous instance are assumed to be generated from a single latent vector. On the other hand, an anomalous instance is assumed to have multiple latent vectors, and its different v...

In this paper we propose a unified framework for structured prediction with latent variables which includes hidden conditional random fields and latent structured support vector machines as special cases. We describe a local entropy approximation for this general formulation using duality, and derive an efficient message passing algorithm that is guaranteed to converge. We demonstrate its effec...

Traditionally, latent factor models have been the most successful techniques to build recommendation systems. While the key is to capture the user interests effectively, most research is focused on learning latent factors under cold-start and data sparsity situations. Our work brings a complementary approach to the previous studies showing that understanding the semantic aspects of latent facto...

LiNGAM has been successfully applied to casual inferences of some real world problems. Nevertheless, basic LiNGAM assumes that there is no latent confounder of the observed variables, which may not hold as the confounding effect is quite common in the real world. Causal discovery for LiNGAM in the presence of latent confounders is a more significant and challenging problem. In this paper, we pr...

Latent variable models can be used to probabilistically “fill-in” missing data entries. The variational autoencoder architecture (Kingma and Welling, 2014; Rezende et al., 2014) includes a “recognition” or “encoder” network that infers the latent variables given the data variables. However, it is not clear how to handle missing data variables in this network. We show how to calculate exactly th...

This paper presents an application of vision-based monitoring of long-term care facility residents. We develop an algorithm to detect events of interest, particularly falls by elderly residents. The algorithm uses a max-margin latent variable approach with spatiotemporal locations of the person in the video as latent variables. The recently developed Action Bank descriptor is utilized as a rich...

Detecting and analyzing stylistic variation in language is relevant to diverse Natural Language Processing applications. In this work, we investigate whether salient dimensions of style variations are embedded in standard distributional vector spaces of word meaning. We hypothesize that distances between embeddings of lexical paraphrases can help isolate style from meaning variations and help i...

Latent variables often play an important role in improving the quality of the learned Bayesian networks and understanding the nature of interactions in the model. The dimensionality of latent variables has significant effect on the representation quality and complexity of the model. The maximum possible dimensionality of a latent variable is a Cartesian product of the state space of its Markov ...

The purposes of this research were 1) to study the effectiveness of Internal Quality Assurance (IQA) of the Health science area, and 2) to study the factors affecting the effectiveness of the IQA of the Health science area. A causal model has been developed by the researcher comprised of the 6 exogenous latent variables: Attitude towards quality assurance, Teamwork, Staff training, Resource suf...