**Recent Papers**

H. Xu, D. Luo and L. Carin, Online
Continuous-Time Tensor Factorization Based on Pairwise Interactive Point
Processes, *Int. Joint Conference on
Artificial Intelligence*, 2018

X. Zhang, X. Yuan and L. Carin, Nonlocal Low-Rank
Tensor Factor Analysis for Image Restoration, *IEEE Computer Vision and Pattern Recognition *(CVPR), 2018.

R. Hultman, K. Ulrich,
B.D. Sachs, C. Blount, D.E. Carlson, N. Ndubuizu,
R.C. Bagot, E.M. Parise, M.-A. T. Vu, N.M. Gallagher, J. Wang, A.J. Silva, K. Deisseroth, S.D. Mague, M.G.
Caron, E.J. Nestler, L. Carin and K. Dzirasa, Brain-wide
Electrical Spatiotemporal Dynamics Encode Depression Vulnerability, *Cell*, March 2018.

H. Xu, D. Luo, X. Chen and L. Carin, Benefits from Superposed Hawkes
Processes, *Artificial Intelligence
and Statistics* (AISTATS), 2018.

R. Zhang, C. Li, C. Chen and L. Carin, Learning Structural Weight
Uncertainty for Sequential Decision-Making, *Artificial Intelligence and Statistics*
(AISTATS), 2018.

Y. Pu, L. Chen, S. Dai, W. Wang, C. Li and L.
Carin, Symmetric
Variational Autoencoder and Connections to Adversarial Learning, *Artificial
Intelligence and Statistics* (AISTATS), 2018.

W. Wang, Z. Gan, W. Wang, D. Shen, J. Huang,
W. Ping, S. Satheesh and L. Carin, Topic Compositional Neural
Language Model, Supplementary
Material, *Artificial Intelligence and
Statistics* (AISTATS), 2018.

D. Shen, Y. Zhang, R. Henao, Q. Su and L.
Carin, Deconvolutional
Latent-Variable Model for Text Sequence Matching, *Proc.* *American Association of
Artificial Intelligence* (AAAI), 2018.

W. Wang, Y. Pu, V.K. Verma,
K. Fan, Y. Zhang, C. Chen, P. Rai and L. Carin, Zero-Shot Learning
via Class-Conditioned Deep Generative Models, *Proc.* *American Association of
Artificial Intelligence* (AAAI), 2018.

Y. Pu, M.R. Min, Z. Gan and L. Carin, Adaptive Feature Abstraction
for Translating Video to Text, *Proc.*
*American Association of Artificial
Intelligence* (AAAI), 2018.

Y. Li, M.R. Min, D. Shen, D. Carlson and L.
Carin, Video
Generation from Text, *Proc.* *American Association of Artificial
Intelligence* (AAAI), 2018.

C. Li, H. Liu, C. Chen, Y. Pu, L. Chen, R.
Henao and L. Carin, ALICE: Towards Understanding
Adversarial Learning for Joint Distribution Matching, Software, *Neural and Information Processing Systems*
(NIPS), 2017

Q. Wei, K. Fai, K.A. Heller and L. Carin, An Inner-Loop
Free Solution to Inverse Problems Using Deep Neural Networks, *Neural and Information Processing Systems*
(NIPS), 2017

Q. Su, X. Liao and L. Carin, A Probabilistic
Framework for Nonlinearities in Stochastic Neural
Networks, Supplementary
Material, *Neural and Information
Processing Systems* (NIPS), 2017

Y. Li, M. Murias, S. Major, G. Dawson, K.
Dzirasa, L. Carin and D.E. Carlson, Targeting EEG/LFP Synchrony
with Neural Nets, *Neural and
Information Processing Systems* (NIPS), 2017

Y. Zhang, D. Shen, G. Wang, Z. Gan, R. Henao,
and L. Carin, Deconvolutional
Paragraph Representation Learning, *Neural
and Information Processing Systems* (NIPS), 2017

Y. Pu, W.Wang, R.
Henao, L. Chen, Z. Gan, C. Li and L. Carin, Adversarial Symmetric
Variational Autoencoder, Supplementary
Material, *Neural and Information
Processing Systems* (NIPS), 2017

Y. Pu, Z. Gan, R. Henao, C. Li, S. Han and L.
Carin, VAE Learning via
Stein Variational Gradient Descent, Supplementary
Material, *Neural and Information
Processing Systems* (NIPS), 2017

Z. Song, Y. Muraoka, R. Fujimaki and L.
Carin, Scalable Model
Selection for Belief Networks, Supplementary
Material, *Neural and Information
Processing Systems* (NIPS), 2017

Z. Gan , L. Chen , W. Wang, Y. Pu, Y. Zhang, H.
Liu, C. Li and L. Carin, Triangle Generative
Adversarial Networks, *Neural and
Information Processing Systems* (NIPS), 2017

N.M. Gallagher, K. Ulrich, A. Talbot, K.
Dzirasa, L. Carin and D.E. Carlson, Cross-Spectral Factor Analysis,
*Neural and Information Processing Systems*
(NIPS), 2017

Z. Gan, Y. Pu, R. Henao, C. Li, X. He and L.
Carin, Learning
Generic Sentence Representations Using Convolutional Neural Networks, *Conf. on Empirical Methods in Natural
Language Processing* (EMNLP), 2017

C. Hu, P. Rai and L. Carin, Deep Generative
Models for Relational Data with Side Information, Supplementary
Material, *Int. Conf. Machine Learning*
(ICML), 2017

Y. Zhang, Z. Gan, K. Fan, Z. Chen, R. Henao,
D. Shen and L. Carin, Adversarial
Feature Matching for Text Generation, Supplementary
Material, *Int. Conf. Machine Learning*
(ICML), 2017

Y. Zhang, C. Chen, Z. Gan, R. Henao and L.
Carin, Stochastic
Gradient Monomial Gamma Sampler, Supplementary
Material, *Int. Conf. Machine Learning*
(ICML), 2017

Z. Xing, S. Hillygus and L.
Carin, Evaluating
U.S. Electoral Representation with a Joint Statistical Model of Congressional
Roll-Calls, Legislative Text, and Voter Registration Data, *Proc.
ACM SIGKDD Conf. Knowledge Discovery and Data Mining*, 2017

Z. Gan, C. Li, C. Chen, Y. Pu, Q. Su, and L.
Carin, Scalable
Bayesian Learning of Recurrent Neural Networks for Language Modeling, *Association for Computational Linguistics*
(ACL), 2017

Z. Gan, C. Gan, X. He, Y. Pu, K. Tran, J.
Gao, L. Carin and L. Deng, Semantic Compositional
Networks for Visual Captioning, Supplementary
Material, *IEEE Conf. Computer Vision
& Pattern Recognition* (CVPR), 2017

S. Sun, C. Chen and L.Carin,
Learning
Structured Weight Uncertainty in Bayesian Neural
Networks, Supplementary
Material, *Artificial Intelligence and
Statistics* (AISTATS), 2017

A. Stevens, Y. Pu, Y. Sun, G. Spell and L.
Carin, Tensor-Dictionary
Learning with Deep Kruskal-Factor Analysis, Supplementary
Material, *Artificial
Intelligence and Statistics* (AISTATS), 2017

Q.
Su, X. Liao, C. Li, Z. Gan and L. Carin, Unsupervised Learning
with Truncated Gaussian Graphical Models, Supplementary
Material, *Proc. American Association of Artificial Intelligence* (AAAI), 2017

**2016**

Y. Zhang, Y. Zhao, L. David, R. Henao and L.
Carin, Dynamic
Poisson Factor Analysis, *IEEE Int.
Conf. Data Mining* (ICDM), 2016

Y. Pu,
Z. Gan, R. Henao, X. Yuan, C. Li,
A. Stevens and L. Carin, Variational
Autoencoder for Deep Learning of Images, Labels and Captions, Supplementary
Material, *Neural and Information
Processing Systems* (NIPS), 2016

Y. Zhang, X. Wang, C. Chen, R. Henao and L.
Carin, Towards
Unifying Hamiltonian Monte Carlo and Slice Sampling, Supplementary
Material, *Neural and Information
Processing Systems* (NIPS), 2016

Z. Song, R. Parr, X. Liao, L. Carin, Linear Feature
Encoding for Reinforcement Learning, Supplementary
Material, *Neural and Information Processing
Systems* (NIPS), 2016

C. Chen, N. Ding, C. Li, Y. Zhang, and L.
Carin, Stochastic
Gradient MCMC with Stale Gradients, Supplementary
Material, *Neural and Information
Processing Systems* (NIPS), 2016

F. Renna, L. Wang, X. Yuan, J. Yang, G.
Reeves, R. Calderbank, L. Carin, and M.R..D. Rodrigues, Classification and Reconstruction of
High-Dimensional Signals from Low-Dimensional Noisy Features in the Presence of
Side Information, *IEEE Trans.
Information Theory, *2017

Q. Su, X. Liao, C. Chen, L. Carin, Nonlinear
Statistical Learning with Truncated Gaussian Graphical Models, *Int. Conf. Machine Learning* (ICML), 2016

J. Song, Z. Gan and L. Carin, Factored Temporal
Sigmoid Belief Networks for Sequence Learning, *Int. Conf. Machine Learning* (ICML), 2016

C. Li, A. Stevens, C. Chen, Y. Pu, Z. Gan and
L. Carin, Learning
Weight Uncertainty with Stochastic Gradient MCMC for Shape Classification, Supplementary
Material, *Computer Vision &
Pattern Recognition* (CVPR), 2016

L. Wang, M. Chen, M. Rodrigues, D. Wilcox, R.
Calderbank and L. Carin, Information-Theoretric Compressive Measurement Design, *IEEE Trans. Pattern Analysis Machine
Intelligence*, 2016

Y. Zhang, R. Henao, C. Li and L. Carin, Bayesian
Dictionary Learning with Gaussian
Processes and Sigmoid Belief Networks, *Int. Joint Conference on
Artificial Intelligence* (IJCAI), 2016.

C. Hu, P. Rai and L. Carin, Non-negative Matrix
Factorization for Discrete Data with Hierarchical Side-Information, *Artificial Intelligence and Statistics*
(AISTATS), 2016

C. Hu, P. Rai and L. Carin, Topic-Based
Embeddings for Learning from Large Knowledge Graphs, Supplementary
Material, *Artificial Intelligence and
Statistics* (AISTATS), 2016

S. Han, X. Liao, D.B. Dunson and L. Carin, Variational Gaussian
Copula Inference, Supplementary
Material, *Artificial Intelligence and
Statistics* (AISTATS), 2016

Z. Song, R. Henao, D. Carlson and L. Carin, Learning Sigmoid
Belief Networks via Monte Carlo Expectation Maximization, Supplementary
Material, *Artificial Intelligence and
Statistics* (AISTATS), 2016

C. Chen, D. Carlson, Z. Gan, C. Li and L.
Carin, Bridging
the Gap Between Stochastic Gradient MCMC and Stochastic Optimization, Supplementary
Material, *Artificial Intelligence and
Statistics* (AISTATS), 2016

Y. Pu, X. Yuan, A. Stevens, C. Li, L. Carin, A Deep Generative
Deconvolutional Image Model, Supplementary
Material, *Artificial Intelligence and
Statistics* (AISTATS), 2016

Y. Kaganovsky, I. Odinaka, D. Carlson and L. Carin, Parallel
Majorization Minimization with Dynamically Restricted Domains for Nonconvex
Optimization, Supplementary
Material, *Artificial Intelligence and
Statistics* (AISTATS), 2016

R. Henao, J.T. Lu, J.E. Lucas, J. Ferranti
and L. Carin, Electronic
Health Record Analysis via Deep Poisson Factor Models, *J. Machine Learning Research*, 2016

C. Li, C. Chen, D. Carlson and L. Carin, Preconditioned
Stochastic Gradient Langevin Dynamics for Deep Neural Networks, Supplementary
Material, *Proc. American Association
of Artificial Intelligence* (AAAI), 2016

C. Li, C. Chen, K. Fan and L. Carin, High-Order
Stochastic Gradient Thermostats for Bayesian Learning of Deep Models, Supplementary
Material, *Proc. American Association
of Artificial Intelligence* (AAAI), 2016

Y. Zhang, R. Henao, L. Carin, J. Zhong and
A.J. Hartemink, Learning
a Hybrid Architecture for Sequence Regression and Annotation, Supplementary
Material, *Proc. American Association
of Artificial Intelligence* (AAAI), 2016

D.E. Carlson, Y.-P. Hsieh, E. Collins, L.
Carin and V. Cevher, Stochastic
Spectral Descent for Discrete Graphical Models, *IEEE Journal of Selected Topics in Signal Processing*, 2016.

**2015**

D.E. Carlson, E. Collins, Y.-P. Hsieh, L.
Carin and V. Cevher, Preconditioned
Spectral Descent for Deep Learning, Supplementary
Material, *Neural and Information
Processing Systems* (NIPS), 2015

P. Rai, C. Hu, R. Henao, L. Carin, Large-Scale Bayesian
Multi-Label Learning via Topic-Based Label Embeddings, Software, *Neural and Information Processing Systems*
(NIPS), 2015

K. Ulrich, D.E. Carlson, K. Dzirasa and L.
Carin, GP
Kernels for Cross-Spectrum Analysis, Supplementary
Material, *Neural and Information
Processing Systems* (NIPS), 2015

C. Chen, N. Ding and L. Carin, On the Convergence of
Stochastic Gradient MCMC Algorithms with High-Order Integrators, Supplementary Material,
*Neural and Information Processing Systems*
(NIPS), 2015

R. Henao, Z. Gan, J. Lu and L. Carin, Deep Poisson Factor
Modeling, Supplementary
Material, *Neural and Information
Processing Systems* (NIPS), 2015

Z. Gan, C. Li, R. Henao, D.E. Carlson and L.
Carin, Deep
Temporal Sigmoid Belief Networks for Sequence Modeling, Supplementary
Material, *Neural and Information
Processing Systems* (NIPS), 2015

Y. Kaganovsky, S. Han, S. Degirmenci, D.G.
Politte, D.J. Brady, J.A. O’Sullivan, and L. Carin, Alternating
Minimization Algorithm with Automatic Relevance Determination for Transmission
Tomography under Poisson Noise, *SIAM
J. Imaging Sciences*, 2015

L. Wang, J. Huang, X. Yuan, K. Krishnamurthy,
J. Greenberg, V. Cevher, M.R.D. Rodrigues, D. Brady, R. Calderbank, and L.
Carin, Signal Recovery and
System Calibration from Multiple Compressive Poisson Measurements, *SIAM J. Imaging Sciences*, 2015

C. Hu, P. Rai and L. Carin, Zero-Truncated
Poisson Tensor Factorization for Massive Binary Tensors, *Uncertainty in Artificial Intelligence*
(UAI), 2015

C. Hu, P. Rai, C. Chen, M. Harding, and L.
Carin, Scalable
Bayesian Non-Negative Tensor Factorization for Massive Count Data, *European Conference on Machine Learning*
(ECML), 2015

X. Yuan, R. Henao, E.L. Tsalik, R.J. Langley
and L. Carin, Non-Gaussian
Discriminative Factor Models via the Max-Margin Rank-Likelihood, Supplementary
Material, *Int. Conf. Machine Learning
*(ICML), 2015

W. Lian, R. Henao, V. Rao, J. Lucas and L.
Carin, A Multitask Point Process
Predictive Model, Supplementary
Material, *Int. Conf. Machine Learning*
(ICML), 2015

Z. Gan, C. Chen, R. Henao, D. Carlson and L.
Carin, Scalable
Deep Poisson Factor Analysis for Topic Modeling, Supplementary
Material, *Int. Conf. Machine Learning*
(ICML), 2015

P. Rai, C. Hu , M. Harding and L. Carin, Scalable
Probabilistic Tensor Factorization for Binary and Count Data, *Int. Joint Conf. on Artificial Intelligence*
(IJCAI), 2015

M. Liu, J.P. How, C. Amato, X. Liao and L.
Carin, Stick-Breaking
Policy Learning in Dec-POMDPs, *Int.
Joint Conf. on Artificial Intelligence* (IJCAI), 2015

W. Lian, R. Talmon, H. Zaveri, L. Carin and
R. Coifman, Multivariate
Time-Series Analysis and Diffusion Maps, *Signal Processing*, 2015

Z. Gan, R. Henao, D. Carlson
and L. Carin, Learning
Deep Sigmoid Belief Networks with Data Augmentation, Supplementary
Material, *Artificial Intelligence and
Statistics* (*AISTATS*), 2015

D. Carlson, V. Cevher and L.
Carin, Stochastic
Spectral Descent for Restricted Boltzmann Machines, Supplementary Material,
*Artificial Intelligence and Statistics*
(*AISTATS*), 2015

X. Yuan, T.-H. Tsai, R. Zhu,
P. Llull, D. Brady, and L. Carin, Compressive
Hyperspectral Imaging with Side Information, Appendix, to appear
in *IEEE* *J. Selected Topics Signal Processing*, 2015

Yi Zhen, P. Rai, H. Zha and L. Carin, Cross-Modal Similarity Learning
via Pairs, Preferences, and Active Supervision, *AAAI Conference on Artificial Intelligence*, 2015

P. Rai, Y. Wang, and L.
Carin, Leveraging
Features and Networks for Probabilistic Tensor Decomposition, *AAAI Conference on Artificial Intelligence*,
2015

W. Lian, P. Rai, E. Salazar,
and L. Carin, Integrating
Features and Similarities: Flexible Models for Heterogeneous Multiview Data,
*AAAI Conference on Artificial
Intelligence*, 2015

**2014**

K. Ulrich, D.E. Carlson, W. Lian, J.S. Borg,
K. Dzirasa and L. Carin, Analysis of
Brain States from Multi-Region LFP Time-Series, Supplementary
Material, *Neural Information
Processing Systems* (NIPS), 2014

J. Yang, X. Liao, M. Chen
and L. Carin, Compressive
Sensing of Signals from a GMM with Sparse Precision Matrices, Supplementary Material,
*Neural Information Processing Systems*
(NIPS), 2014

D.E. Carlson, J. Schaich Borg, K. Dzirasa, and L. Carin, On the Relationship
Between LFP & Spiking Data, Supplementary
Material, *Neural Information
Processing Systems* (NIPS), 2014

S. Han , L. Du , E. Salazar
and L. Carin, Dynamic
Rank Factor Model for Text Streams, Supplementary
Material, *Neural Information
Processing Systems* (NIPS), 2014

R. Henao, X. Yuan and L.
Carin, Bayesian
Nonlinear Support Vector Machines and Discriminative Factor Modeling, Supplementary
Material, *Neural Information
Processing Systems* (NIPS), 2014

X. Yuan, V. Rao, S. Han and
L. Carin, Hierarchical
Infinite Divisibility for Multiscale Shrinkage, (Supplementary
Material), (Code),
accepted for publication in *IEEE Trans.
Signal Processing*

J. Yang, X. Liao, X. Yuan,
P. Llull, D.J. Brady, G. Sapiro, and L. Carin, Compressive Sensing
by Learning a Gaussian Mixture Model from Measurements, accepted for
publication in *IEEE Trans. Image
Processing*

L. Wang, A. Razi, M.D. Rodrigues, R. Calderbank and L. Carin, Nonlinear Information-Theoretic
Compressive Measurement Design (Supplementary Material),
*Proc. Int. Conf. Machine Learning*
(ICML), 2014

P. Rai, Y.Wang,
S. Guoz, G. Chen, D. Dunson and L. Carin, Scalable Bayesian Low-Rank
Decomposition of Incomplete Multiway Tensors (Supplementary Material),
*Proc. Int. Conf. Machine Learning*
(ICML), 2014

W. Lian, V. Rao, B.
Eriksson, L. Carin, Modeling
Correlated Arrival Events with Latent Semi-Markov Processes, *Proc. Int. Conf. Machine Learning*
(ICML), 2014

J. Yang, X. Yuan, X. Liao,
P. Llull, D.J. Brady, G. Sapiro and L. Carin, Video Compressive
Sensing Using Gaussian Mixture Models, (Software), to
appear in *IEEE Trans. Image Processing*,
2014

H. Zhang and L. Carin, Multi-Shot
Imaging: Joint Alignment, Deblurring and Resolution-Enhancement, *IEEE Computer Vision and Pattern Recognition
*(CVPR), 2014

X. Yuan, P. Llull, X. Liao,
J. Yang, G. Sapiro, D.J. Brady and L. Carin, Low-Cost Compressive
Sensing for Color Video and Depth, *IEEE
Computer Vision and Pattern Recognition* (CVPR), 2014

C. Hu, E. Ryu, D. Carlson,
Y. Wang and L. Carin, Latent Gaussian
Models for Topic Modeling, *Artificial Intelligence & Statistics *(AISTATS),
2014

X. Liao, H. Li, and L.
Carin, Generalized
Alternating Projection for Weighted-ℓ2,1 Minimization with Applications
to Model-Based Compressive Sensing, to appear in *SIAM J. Imaging Science, *2014

L. Wang, D. Carlson, M.R.D.
Rodrigues, R. Calderbank and L. Carin, A Bregman Matrix and the
Gradient of Mutual Information for Vector Poisson and Gaussian Channels, to
appear in *IEEE Trans. Information Theory*,
2014

F. Renna, R. Calderbank, L.
Carin, and M.R.D. Rodrigues, Reconstruction
of Signals Drawn from a Gaussian Mixture via Noisy Compressive Measurements, to appear *IEEE
Trans. Signal Processing*, 2014.

Z. Xing, B. Nicholson, M.
Jimenez, T. Veldman, L. Hudson, J. Lucas, D. Dunson, A.K. Zaas,
C.W. Woods, G.S. Ginsburg and L. Carin, Bayesian Modeling of
Space-Time Properties of Infectious Disease in a College Student Population,
to appear in *J. Applied Statistics, *2014

M. Zhou and L. Carin, Negative Binomial
Process Count and Mixture Modeling, to appear in *IEEE Trans. Pattern
Analysis Machine Intelligence, *2014

**2013**

T. Campbell, M. Liu, B. Kulis, J.P. How, L. Carin, Dynamic Clustering
via Asymptotics of the Dependent Dirichlet Process
Mixture, *Neural Information
Processing Systems* (NIPS), 2013

L. Wang, D. Carlson, M.D.
Rodrigues, D. Wilcox, R. Calderbank and L. Carin, Designed Measurements
for Vector Count Data, (Supplementary
Material), *Neural Information
Processing Systems* (NIPS), 2013

S. Han, X. Liao and L.
Carin, Integrated
Non-Factorized Variational Inference (Supplementary
Material), *Neural Information
Processing Systems* (NIPS), 2013

D. Carlson, V. Rao, J.
Vogelstein and L. Carin, Real-Time Inference
for a Gamma Process Model of Neural Spiking, *Neural Information Processing Systems* (NIPS), 2013

D. Carlson, J. Vogelstein, Q.Wu, W. Lian, M. Zhou, C.R. Stoetzner,
D. Kipke, D. Weber, D. Dunson and L. Carin, Sorting
Electrophysiological Data via Dictionary Learning & Mixture Modeling, *IEEE Trans. Biomedical Engineering*, 2013

E. Salazar, D.B. Dunson, and
L. Carin, Analysis
of Space-Time Relational Data with Application to Legislative Voting, *Computational Statistics and Data Analysis, *2013

M. Liu, X. Liao and L.
Carin, Online
Expectation Maximization for Reinforcement Learning in POMDPs, *Prof. Int. Joint Conf. Artificial
Intelligence* (IJCAI), 2013

P. Llull, X. Liao, X. Yuan,
J. Yang, D. Kittle, L. Carin, G. Sapiro and D.J. Brady, Coded Aperture
Compressive Temporal Imaging, *Optics
Express*, 2013.

E. Salazar, R. Bogdan, A. Gorka, A.R. Hariri and L. Carin, Exploring the
Mind: Integrating Questionnaires and fMRI, *Proc.
Int. Conf. Machine Learning* (ICML), 2013

A. Rajwade,
D. Kittle, T.-H. Tsai, D. Brady and L. Carin, Coded Hyperspectral
Imaging and Blind Compressive Sensing, *SIAM
J. Imaging Science, *2013

B. Chen, G. Polatkan, G. Sapiro, D. Blei, D. Dunson and L. Carin, Deep Learning with
Hierarchical Convolutional Factor Analysis (supplementary material),
*IEEE Trans. Pattern Analysis &
Machine Intelligence*, 2013.

E. Wang, E. Salazar, D.
Dunson and L. Carin, Spatio-Temporal
Modeling of Legislation and Votes, *Bayesian
Analysis*, 2013.

**2012**

M. Zhou and L. Carin, Augment-and-Conquer
Negative Binomial Processes, *Proc.
Neural and Information Processing Systems *(NIPS), 2012

X. Zhang and L. Carin, Joint Modeling of
a Matrix with Associated Text via Latent
Binary Features (Supplementary
Material), *Proc. Neural and
Information Processing Systems *(NIPS), 2012

L. Li, X. Zhang, M. Zhou and
L. Carin, Nested
Dictionary Learning for Hierarchical Organization of Imagery and Text (Supplementary
Material), *Proc. Uncertainty in
Artificial Intelligence* (UAI), 2012

M. Ding, L. He, D. Dunson
and L. Carin, Nonparametric
Bayesian Segmentation of Multivariate Inhomogeneous Space-Time Poisson Process,
*Bayesian
Analysis, *2012

W.R.
Carson, M. Chen, M.R.D. Rodrigues, R. Calderbank and L. Carin, Communications Inspired
Projection Design with Application to Compressive Sensing, *SIAM J. Imaging Sciences, *2012

Y. Wang and L. Carin, Levy Measure
Decompositions for the Beta and Gamma Processes (Supplementary
material), *Proc. Int. Conf. Machine
Learning* (ICML), 2012

S. Han, X. Liao, L. Carin, Cross-Domain
Multitask Learning with Latent Probit Models, *Proc. Int. Conf. Machine Learning* (ICML), 2012

M. Chen, W. Carson, M.
Rodrigues, R. Calderbank and L. Carin, Communications Inspired
Linear Discriminant Analysis, *Proc. Int.
Conf. Machine Learning* (ICML), 2012

M. Zhou, L. Li, D. Dunson,
and L. Carin, Lognormal
and Gamma Mixed Negative Binomial Regression (Supplementary
Material), *Proc. Int. Conf. Machine
Learning* (ICML), 2012

E. Salazar, M.S. Cain, E.F.
Darling, S.R. Mitroff and L.Carin,
Inferring Latent
Structure From Mixed Real and Categorical Relational Data, *Proc. Int. Conf. Machine Learning* (ICML), 2012

X. Chen, M. Zhou, and L. Carin, The Contextual
Focused Topic Model, *Proc. ACM SIGKDD
Conf. Knowledge Discovery and Data Mining*, 2012

J. Silva and L. Carin,** **Active Learning for
Online Bayesian Matrix Factorization, *Proc.
ACM SIGKDD Conf. Knowledge Discovery and Data Mining*, 2012

M. Zhou, L. Hannah, D.
Dunson and L. Carin, Beta-Negative
Binomial Process and Poisson Factor Analysis, AISTATS 2012

**2011**

X. Zhang, D.B. Dunson, and
L. Carin, Hierarchical
Topic Modeling for Analysis of Time-Evolving Personal Choices, (Supplementary
Materials), *Proc. Neural and
Information Processing Systems *(NIPS), 2011

L. Ren, Y. Wang, D. Dunson
and L. Carin, The
Kernel Beta Process (Supplementary
Material), *Proc. Neural and
Information Processing Systems *(NIPS), 2011

B. Chen, D.E. Carlson and L.
Carin, On the
Analysis of Multi-Channel Neural Spike Data, *Proc. Neural and Information Processing Systems *(NIPS), 2011

M. Chen, A. Zaas, C. Woods, G.S. Ginsburg, J. Lucas, D. Dunson and L.
Carin, Predicting
Viral Infection from High-Dimensional Biomarker Trajectories, *J. Am. Statistical Association, *2011

Z. Xing, M. Zhou, A. Castrodad, G. Sapiro and L. Carin, Dictionary Learning for
Noisy and Incomplete Hyperspectral Images, *SIAM J. Imaging Sciences, *2011

M.
Liu, X. Liao and L. Carin, The Infinite
Regionalized Policy Representation, *Proc.
Int. Conf. Machine Learning* (ICML), 2011

J.
Paisley, L. Carin and D. Blei, Variational
Inference for Stick-Breaking Beta Process Priors, *Proc. Int. Conf. Machine Learning* (ICML), 2011

X.
Zhang, D.B. Dunson and L. Carin, Tree-Structured
Infinite Sparse Factor Model, (Supplemental
Material), *Proc. Int. Conf. Machine
Learning* (ICML), 2011

L.
Li, M. Zhou, G. Sapiro and L. Carin, On the Integration of
Topic Modeling and Dictionary Learning, *Proc.
Int. Conf. Machine Learning* (ICML), 2011

H.
Chen, D.B. Dunson and L. Carin, Topic Modeling with
Nonparametric Markov Tree, *Proc. Int.
Conf. Machine Learning* (ICML), 2011

B.
Chen, G. Polatkan, G. Sapiro, D. Dunson and L. Carin,
The Hierarchical Beta Process
for Convolutional Factor Analysis and Deep Learning, *Proc. Int. Conf. Machine Learning* (ICML), 2011

M. Zhou, H. Yang, G. Sapiro, D. Dunson and L. Carin, Dependent
Hierarchical Beta Process for Image Interpolation and Denoising, AISTATS,
2011

X. Ding, L. He and L. Carin, Bayesian Robust Principal Component
Analysis, *IEEE Trans. Image
Processing, *2011

M. Zhou, H. Chen, J. Paisley, L. Ren, L. Li, Z. Xing,
D. Dunson, G. Sapiro and L. Carin, Nonparametric
Bayesian Dictionary Learning for Analysis of Noisy and Incomplete Images, *IEEE Trans. Image Processing, *2011

L. Ren, L. Du, L. Carin and
D. Dunson, Logistic
Stick-Breaking Process, *J. Machine
Learning Research *(code is here), 2011

L. Carin, D. Liu and B. Guo,
Coherence,
Compressive Sensing and Random Sensor Arrays, to appear in *IEEE Antennas and Propagation Magazine*,
2011

**2010**

C. Wang, X. Liao, D. Dunson
and L. Carin, Multi-Task
Learning for Incomplete Data, *J.
Machine Learning Research, *2010

E. Wang, D. Liu, J. Silva,
D. Dunson and L. Carin, Joint Analysis of
Time-Evolving Binary Matrices and Associated Documents, *Proc.* *Neural and Information Processing Systems* (NIPS), 2010

M. Zhou, C. Wang, M. Chen,
J. Paisley, D. Dunson and L. Carin, Nonparametric
Bayesian Matrix Completion, *2010 IEEE
Sensor Array and Multichannel Signal Processing Workshop*

L. Du, M. Chen, J. Lucas and
L. Carin, Sticky Hidden
Markov Modeling of Comparative Genomic Hybridization, *IEEE Trans. Signal Processing*, 2010.

M. Chen, D. Carlson, A. Zaas,
C. Woods, G.S. Ginsburg, J. Lucas and L. Carin, Detection of viruses via
statistical gene-expression analysis, accepted for publication in *IEEE Trans. Biomedical Engineering*

J. Paisley, A. Zaas, C.W. Woods,
G.S. Ginsburg and L. Carin, A Stick-Breaking
Construction of the Beta Process, *Int.
Conf. Machine Learning* (ICML), June 2010

J. Paisley, X. Liao and L. Carin, Active learning and
basis selection for kernel-based linear models: A Bayesian perspective, *IEEE Trans. Signal Processing, *2010.

B. Chen, M. Chen, J.
Paisley, A. Zaas, C. Woods, G.S. Ginsburg, A. Hero
III, J. Lucas, D. Dunson and L. Carin, Nonparametric Bayesian
Factor Analysis: Application to Time-Evolving Viral Gene-Expression Data, *BMC Bioinformatics, *2010

M. Chen, J. Silva, J.
Paisley, C. Wang, D. Dunson and L. Carin, Compressive Sensing on
Manifolds Using a Nonparametric Mixture of Factor Analyzers: Algorithm and
Performance Bounds, *IEEE Trans.
Signal Processing, *2010

L. Ren, D. Dunson, S.
Lindroth and L. Carin, Dynamic
Nonparametric Bayesian Models for Analysis of Music, *J. American Statistical Association* (JASA), 2010.

I. Pruteanu-Malinici,
L. Ren, J. Paisley, E. Wang and L. Carin, Hierarchical Bayesian
Modeling of Topics in Time-Stamped Documents, *IEEE Trans. Pattern Analysis Machine Intelligence*, 2010.

J. Paisley and L. Carin, Hidden Markov Models with
Stick-Breaking Priors, *IEEE Trans.
Signal Processing, *2010

**2009**

M. Zhou, H. Chen, J.
Paisley, L. Ren, G. Sapiro and L. Carin, Non-Parametric
Bayesian Dictionary Learning for Sparse Image Representations, *Neural and Information Processing Systems*
(NIPS), 2009.

L. Du, L. Ren, D. Dunson and
L. Carin, A
Bayesian Model for Simultaneous Image Clustering, Annotation and Object
Segmentation, *Neural and Information Processing Systems* (NIPS), 2009.

C. Cai, X. Liao, and L.
Carin, Learning
to Explore and Exploit in POMDPs, *Neural and Information Processing
Systems* (NIPS), 2009.

L. He, H. Chen and L. Carin,
Tree-Structured
Compressive Sensing with Variational Bayesian Analysis, *IEEE Signal Processing Letters, *2009

J. Paisley and L. Carin, Nonparametric Factor
Analysis with Beta Process Priors, *Proc.
Int. Conf. Machine Learning *(ICML), 2009

H. Li, X. Liao and L. Carin,
Multi-task
Reinforcement Learning in Partially Observable Stochastic Environments, *J*. *Machine
Learning Research*, 2009

L. Carin, On the Relationship
Between Compressive Sensing and Random Sensor Arrays, *IEEE Antennas & Propagation Magazine*, October 2009.

Q. Liu, X. Liao, H. Li, J.
Stack and L. Carin, Semi-Supervised
Multitask Learning, *IEEE Trans.
Pattern Analysis & Machine Intelligence, *2009

L. He and L. Carin, Exploiting Structure in
Wavelet-Based Bayesian Compressive Sensing, *IEEE Trans. Signal Processing*, Sept 2009