Max-Margin Contrastive Learning

Efficient Max-Margin Metric Learning

Efficient learning of an appropriate distance metric is an increasingly important problem in machine learning. However, current methods are limited by scalability issues or are unsuited to use with general similarity/dissimilarity constraints. In this paper, we propose an efficient metric learning method based on the max-margin framework with pairwise constraints that has a strong generalizatio...

Learning Max-Margin Tree Predictors

Structured prediction is a powerful framework for coping with joint prediction of interacting outputs. A central difficulty in using this framework is that often the correct label dependence structure is unknown. At the same time, we would like to avoid an overly complex structure that will lead to intractable prediction. In this work we address the challenge of learning tree structured predict...

23 : Max - margin Learning of Graphical Models

Max-Margin Multiple-Instance Dictionary Learning

Dictionary learning has became an increasingly important task in machine learning, as it is fundamental to the representation problem. A number of emerging techniques specifically include a codebook learning step, in which a critical knowledge abstraction process is carried out. Existing approaches in dictionary (codebook) learning are either generative (unsupervised e.g. k-means) or discrimina...

Contrastive Max-Sum Opinion Summarization

People can reach all kinds of information online including reviews and comments on products, movies, holiday destinations and so on. However, one usually need to go through the reviews to have an objective opinion the positive and the negative aspects of the item reviewed. We aim to provide a method that will extract positive and negative opinions on a specific aspect and compare them in an att...