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- Stephen Mussmann, Stefano Ermon
- ICML
- 2016

A large class of commonly used probabilistic models known as log-linear models are defined up to a normalization constant.Typical learning algorithms for such models require solving a sequence of probabilistic inference queries. These inferences are typically intractable, and are a major bottleneck for learning models with large output spaces. In this… (More)

- Stephen Mussmann, John Moore, Joseph J. Pfeiffer, Jennifer Neville
- SNAKDD
- 2014

Due to the widespread interest in networks as a representation to investigate the properties of complex systems, there has been a great deal of interest in generative models of graph structure that can capture the properties of networks observed in the real world. Recent models have focused primarily on accurate characterization of sparse networks with… (More)

Mining the underlying patterns in gigantic and complex data is of great importance to data analysts. In this paper, we propose a motion pattern approach to mine frequent behaviors in trajectory data. Motion patterns, defined by a set of highly similar flow vector groups in a spatial locality, have been shown to be very effective in extracting dominant… (More)

Due to the recent availability of large complex networks, considerable analysis has focused on understanding and characterizing the properties of these networks. Scalable generative graph models focus on modeling distributions of graphs that match real world network properties and scale to large datasets. Much work has focused on modeling networks with a… (More)

- Stephen Mussmann, Daniel Levy, Stefano Ermon
- ArXiv
- 2017

Inference in log-linear models scales linearly in the size of output space in the worst-case. This is often a bottleneck in natural language processing and computer vision tasks when the output space is feasibly enumerable but very large. We propose a method to perform inference in log-linear models with sublinear amortized cost. Our idea hinges on using… (More)

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