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- Dmitry M. Malioutov, MÃ¼jdat Ã‡etin, Alan S. Willsky
- IEEE Transactions on Signal Processing
- 2005

We present a source localization method based on a sparse representation of sensor measurements with an overcomplete basis composed of samples from the array manifold. We enforce sparsity by imposingâ€¦ (More)

- Venkat Chandrasekaran, Benjamin Recht, Pablo A. Parrilo, Alan S. Willsky
- Foundations of Computational Mathematics
- 2012

In applications throughout science and engineering one is often faced with the challenge of solving an ill-posed inverse problem, where the number of available measurements is smaller than theâ€¦ (More)

- Andy Tsai, Anthony J. Yezzi, +5 authors Alan S. Willsky
- IEEE Transactions on Medical Imaging
- 2003

We propose a shape-based approach to curve evolution for the segmentation of medical images containing known object types. In particular, motivated by the work of Leventon, Grimson, and Faugerasâ€¦ (More)

- Venkat Chandrasekaran, Sujay Sanghavi, Pablo A. Parrilo, Alan S. Willsky
- SIAM Journal on Optimization
- 2011

Suppose we are given a matrix that is formed by adding an unknown sparse matrix to an unknown low-rank matrix. Our goal is to decompose the given matrix into its sparse and low-rank components. Suchâ€¦ (More)

- Erik B. Sudderth, Alexander T. Ihler, Michael Isard, William T. Freeman, Alan S. Willsky
- Commun. ACM
- 2003

Continuous quantities are ubiquitous in models of real-world phenomena, but are surprisingly difficult to reason about automatically. Probabilistic graphical models such as Bayesian networks andâ€¦ (More)

- Martin J. Wainwright, Tommi S. Jaakkola, Alan S. Willsky
- IEEE Transactions on Information Theory
- 2005

We develop and analyze methods for computing provably optimal maximum a posteriori probability (MAP) configurations for a subclass of Markov random fields defined on graphs with cycles. Byâ€¦ (More)

- Martin J. Wainwright, Tommi S. Jaakkola, Alan S. Willsky
- IEEE Transactions on Information Theory
- 2002

We introduce a new class of upper bounds on the log partition function of a Markov random field (MRF). This quantity plays an important role in various contexts, including approximating marginalâ€¦ (More)

- Myung Jin Choi, Joseph J. Lim, Antonio Torralba, Alan S. Willsky
- 2010 IEEE Computer Society Conference on Computerâ€¦
- 2010

There has been a growing interest in exploiting contextual information in addition to local features to detect and localize multiple object categories in an image. Context models can efficiently ruleâ€¦ (More)

- Alexander T. Ihler, John W. Fisher, Randolph L. Moses, Alan S. Willsky
- IEEE Journal on Selected Areas in Communications
- 2005

Automatic self-localization is a critical need for the effective use of ad hoc sensor networks in military or civilian applications. In general, self-localization involves the combination of absoluteâ€¦ (More)

The hierarchical Dirichlet process hidden Markov model (HDP-HMM) is a flexible, nonparametric model which allows state spaces of unknown size to be learned from data. We demonstrate some limitationsâ€¦ (More)