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- Shihao Ji, Ya Xue, Lawrence Carin
- IEEE Transactions on Signal Processing
- 2008

The data of interest are assumed to be represented as N-dimensional real vectors, and these vectors are compressible in some linear basis B, implying that the signal can be reconstructed accurately… (More)

- Balaji Krishnapuram, Lawrence Carin, Mário A. T. Figueiredo, Alexander J. Hartemink
- IEEE Transactions on Pattern Analysis and Machine…
- 2005

Recently developed methods for learning sparse classifiers are among the state-of-the-art in supervised learning. These methods learn classifiers that incorporate weighted sums of basis functions… (More)

- Mingyuan Zhou, Haojun Chen, +6 authors Lawrence Carin
- IEEE Transactions on Image Processing
- 2012

Nonparametric Bayesian methods are considered for recovery of imagery based upon compressive, incomplete, and/or noisy measurements. A truncated beta-Bernoulli process is employed to infer an… (More)

- Ya Xue, Xuejun Liao, Lawrence Carin, Balaji Krishnapuram
- Journal of Machine Learning Research
- 2007

Consider the problem of learning logistic-regression models for multiple classification tasks, where the training data set for each task is not drawn from the same statistical distribution. In such a… (More)

- Lihan He, Lawrence Carin
- IEEE Transactions on Signal Processing
- 2009

Bayesian compressive sensing (CS) is considered for signals and images that are sparse in a wavelet basis. The statistical structure of the wavelet coefficients is exploited explicitly in the… (More)

- Shihao Ji, David B. Dunson, Lawrence Carin
- IEEE Transactions on Signal Processing
- 2009

Compressive sensing (CS) is a framework whereby one performs N nonadaptive measurements to constitute a vector v isin RN used to recover an approximation u isin RM desired signal u isin RM with N <<… (More)

- John William Paisley, Lawrence Carin
- ICML
- 2009

We propose a nonparametric extension to the factor analysis problem using a beta process prior. This beta process factor analysis (BP-FA) model allows for a dataset to be decomposed into a linear… (More)

Non-parametric Bayesian techniques are considered for learning dictionaries for sparse image representations, with applications in denoising, inpainting and compressive sensing (CS). The beta process… (More)

- Mingyuan Zhou, Lauren Hannah, David B. Dunson, Lawrence Carin
- AISTATS
- 2012

A beta-negative binomial (BNB) process is proposed, leading to a beta-gamma-Poisson process, which may be viewed as a “multiscoop” generalization of the beta-Bernoulli process. The BNB process is… (More)

- Xinghao Ding, Lihan He, Lawrence Carin
- IEEE Transactions on Image Processing
- 2011

A hierarchical Bayesian model is considered for decomposing a matrix into low-rank and sparse components, assuming the observed matrix is a superposition of the two. The matrix is assumed noisy, with… (More)