**Recent Papers**

R. Zhang, C. Chen, Z. Gan, W. Wang, D. Shen,
G. Wang, Z. Wen and L. Carin, Improving
Adversarial Text Generation by Modeling the Distant Future, *Conf.* *Association
for Computational Linguistics* (ACL), 2020

P. Cheng, M.R. Min, D. Shen, C. Malon, Y. Zhang, Y. Li and L. Carin, Improving Disentangled
Text Representation Learning with Information-Theoretic Guidance, *Conf.* *Association for Computational Linguistics*
(ACL), 2020

Y. Lu, Y. Jia, J. Wang, B. Li, W. Chai, L.
Carin and S. Velipasalar, Enhancing Cross-Task
Black-Box Transferability of Adversarial Examples with
Dispersion Reduction, *IEEE
Computer Vision and Pattern Recognition* (CVPR), 2020

W. Hao, C. Li, X. Li, L. Carin, and J. Gao, Towards
Learning a Generic Agent for Vision-and-Language Navigation via Pre-training,
*IEEE Computer Vision and Pattern Recognition*
(CVPR), 2020

J. Zhang, R. Zhang, L. Carin and C. Chen, Stochastic
Particle-Optimization Sampling and the Non-Asymptotic Convergence Theory, *Artificial
Intelligence and Statistics* (AISTATS), 2020

R. Zhang, C. Chen, Z. Gan, Z. Wen, W. Wang
and L. Carin, Nested-Wasserstein
Self-Imitation Learning for Sequence Generation, *Artificial Intelligence
and Statistics* (AISTATS), 2020

P. Chapfuwa, C. Li, N. Mehta, L. Carin
and R. Henao, Survival Cluster Analysis,
*ACM Conference on Health, Inference, and Learning* (CHIL), 2020

S. Lobel, C. Li, J. Gao and L. Carin, RACT: Towards Amoritized Ranking-Critical Training for Collaborative
Filtering, *Int. Conf. Learning Representations* (ICLR), 2020

N. Inkawhich, K.J.
Liang, L. Carin and Y. Chen, Transferable
Perturbations of Deep Feature Distributions, *Int.
Conf. Learning Representations* (ICLR), 2020

D.E. Range, D. Dov, S.Z. Kovalsky, R. Henao,
L. Carin and J. Cohen, Application
of a Machine Learning Algorithm to Predict Malignancy in Thyroid Cytopathology,
*Cancer Cytopathol*, Feb. 2020

M. Zhao, Y. Cong, S. Dai and L. Carin, Bridging Maximum Likelihood
and Adversarial Learning via α-Divergence, *Proc.* *American Association of
Artificial Intelligence* (AAAI), 2020

L. Chen, K. Bai, C. Tao, Y. Zhang, G. Wang,
W. Wang, R. Henao and L. Carin, Sequence Generation
with Optimal-Transport-Enhanced** **Reinforcement
Learning, *Proc.* *American Association of
Artificial Intelligence* (AAAI), 2020

P. Cheng, Y. Li, X. Zhang, L. Chen, D.
Carlson and L. Carin, Dynamic Embedding
on Textual Networks via a Gaussian Process, *Proc.* *American Association of
Artificial Intelligence* (AAAI), 2020

W. Wang, H. Xu, Z. Gan, B. Li, G. Wang, L.
Chen, Q. Yang, W. Wang and L. Carin, Graph-Driven
Generative Models for Heterogeneous Multi-Task Learning, *Proc.* *American Association of Artificial Intelligence* (AAAI), 2020

Y. Li, C. Li, Y. Zhang, X. Li, G. Zheng, L.
Carin and J. Gao, Complementary
Auxiliary Classifiers for Label-Conditional Text Generation, *Proc.* *American Association of Artificial Intelligence* (AAAI), 2020

**2019**

C. Tao, L. Chen, S. Dai, J. Chen, K. Bai, D.
Wang, J. Feng, W. Lu, G. Bobashev and L. Carin, On Fenchel Mini-Max Learning, *Neural and Information Processing Systems* (NeurIPS),
2019

R. Zhang, T. Yu, Y. Shen, H. Jin, C. Chen and
L. Carin, Text-Based
Interactive Recommendation via Constraint-Augmented Reinforcement Learning, *Neural and
Information Processing Systems* (NeurIPS), 2019

B. Li, C. Chen, W. Wang and L. Carin, Certified
Adversarial Robustness with Additive Noise, *Neural and Information Processing Systems*
(NeurIPS), 2019

K.J. Liang, G. Wang, Y. Li, R. Henao and L.
Carin, Kernel-Based
Approaches for Sequence Modeling: Connections to Neural Methods, *Neural and
Information Processing Systems* (NeurIPS), 2019

H. Xu, D. Luo and L. Carin, Scalable Gromov-Wasserstein Learning for Graph Partitioning and
Matching, *Neural
and Information Processing Systems* (NeurIPS), 2019

W. Wang, C. Tao, Z. Gan, G. Wang, L. Chen, X.
Zhang, R. Zhang, Q. Yang, R. Henao and L. Carin, Improving
Textual Network Learning with Variational Homophilic Embeddings, *Neural and Information Processing Systems*
(NeurIPS), 2019

Q. Yang, Z. Huo, W. Wang, H. Huang and L.
Carin, Ouroboros: On
Accelerating Training of Transformer-Based Language Models, *Neural and
Information Processing Systems* (NeurIPS), 2019

Q. Yang, Z. Huo, D. Shen, Y. Cheng, W. Wang,
G. Wang, and L. Carin, An
End-to-End Generative Architecture for Paraphrase Generation, *Empirical
Methods in Natural Language Processing* (EMNLP), 2019

D. Dov, S.Z. Kovalsky, J. Cohen, D.E. Range,
R. Henao and L. Carin, Thyroid Cancer
Malignancy Prediction From Whole Slide Cytopathology Images, *Machine Learning in Healthcare* (MLHC), 2019

P. Cheng, D. Shen, D. Sundararaman, X. Zhang,
Q. Yang, M. Tang, A. Celikyilmaz and L. Carin, Learning
Compressed Sentence Representations for On-Device Text Processing, *Association for Computational Linguistics*
(ACL), 2019

D. Shen, A. Celikyilmaz,
Y. Zhang, L. Chen, X. Wang, J. Gao and L. Carin, Towards Generating Long and
Coherent Text with Multi-Level Latent Variable Models, Supplementary Material, *Association for
Computational Linguistics* (ACL), 2019

L. Chen, G. Wang, C. Tao, D. Shen, P. Cheng,
X. Zhang, W. Wang, Y. Zhang and L. Carin, Improving
Textual Network Embedding with Global Attention via Optimal Transport, *Association for Computational Linguistics*
(ACL), 2019

X. Zhang, Y. Yang, S. Yuan, D. Shen and L.
Carin, Syntax-Infused
Variational Autoencoder for Text Generation, *Association for Computational Linguistics* (ACL), 2019

C. Tao, S. Dai, L. Chen, K.Bai,
J. Chen, C. Liu, R. Zhang, G. Bobashev and L. Carin, Variational Annealing of
GANs: A Langevin Perspective, Supplementary
Material, *Int. Conf. Machine Learning*
(ICML), 2019

N. Mehta, L. Carin and P. Rai, Stochastic Blockmodels Meet Graph Neural Networks, Supplementary
Material, *Int. Conf. Machine Learning*
(ICML), 2019

Z. Song, R.E. Parr and L. Carin, Revisiting the Softmax Bellman Operator: New Benefits and New Perspective,
Supplementary
Material, *Int. Conf. Machine Learning*
(ICML), 2019

H. Xu, D. Luu, H.
Zha and L. Carin, Gromov-Wasserstein Learning for Graph Matching and Node
Embedding, *Int. Conf. Machine
Learning* (ICML), 2019

C. Liu, J. Zhuo, P.
Cheng, R. Zhang, J. Zhu and L. Carin, Understanding and Accelerating
Particle-Based Variational Inference, *Int.
Conf. Machine Learning* (ICML), 2019

Y. Li, Z. Gan, Y. Shen, J. Liu, Y. Cheng, Y.
Wu, L. Carin, D. Carlson and J. Gao, StoryGAN:
A Sequential Conditional GAN for Story Visualization*, IEEE Computer Vision and Pattern Recognition *(CVPR), 2019

H. Fu, C. Li, X. Liu, J. Gao, A. Celikyilmaz and L. Carin, Cyclical Annealing Schedule: A
Simple Approach to Mitigating KL Vanishing, *Annual Conf. North American Chapter of the Assoc. Computational
Linguistics *(NAACL), 2019

W. Wang, Z. Gan, H. Xu, R. Zhang, G. Wang, D.
Shen, C. Chen, and L. Carin, Topic-Guided
Variational Autoencoders for Text Generation, *Annual Conf. North American Chapter of the
Assoc. Computational Linguistics *(NAACL), 2019

B. Li, C. Chen, H. Liu, L. Carin, Towards More
Practical Stochastic Gradient MCMC in Differential Privacy, *Artificial Intelligence and Statistics*
(AISTATS), 2019

R. Zhang, Z. Wen, C. Chen, C. Fang, T. Yu,
and L. Carin, Scalable
Thompson Sampling via Optimal Transport, *Artificial Intelligence and Statistics*
(AISTATS), 2019

C. Li, K. Bai, J. Li, G. Wang, C. Chen and L.
Carin, Adversarial
Learning of a Sampler Based on an Unnormalized Distribution, *Artificial
Intelligence and Statistics* (AISTATS), 2019

L. Chen, Y. Zhang, R. Zhang, C. Tao, Z. Gan,
H. Zhang, B. Li, D. Shen, C. Chen and L. Carin, Improving
Sequence-to-Sequence Learning via Optimal Transport, *Int. Conf. Learning Representations* (ICLR), 2019

Y. Cong, M. Zhao, K. Bai and L. Carin, GO Gradient for
Expectation-Based Objectives, *Int.
Conf. Learning Representations* (ICLR), 2019

C. Li, C. Chen, Y. Pu, R. Henao and L. Carin,
Communication-Efficient
Stochastic Gradient MCMC for Neural Networks, Supplemental
Material, *Proc.*
*American Association of Artificial
Intelligence* (AAAI), 2019

**2018**

L. Carin and M. Pencina, On Deep
Learning for Medical Image Analysis, *J.
Am. Medical Association* (JAMA), Accompanying Video,
Sept. 18, 2018

X. Zhang, R. Henao, Z. Gan, Y. Li and L.
Carin, Multi-Label
Learning from Medical Plain Text with Convolutional Residual Models, *Machine
Learning in Healthcare* (MLHC), 2018

H. Xu, W. Wang, W. Liu and L. Carin, Distilled Wasserstein
Learning for Word Embedding and Topic Modeling, *Neural and Information Processing Systems*
(NeurIPS), 2018

X. Zhang, Y. Li, D. Shen and L. Carin, Diffusion Maps for
Textual Network Embedding, *Neural and Information Processing Systems* (NeurIPS), 2018

L. Chen, S. Dai, C. Tao, D. Shen, Z. Gan, H.
Zhang, Y. Zhang and L. Carin, Adversarial Text
Generation via Feature-Mover’s Distance, *Neural and Information Processing Systems* (NeurIPS),
2018

D. Shen, X. Zhang, R. Henao, L. Carin, Improved
Semantic-Aware Network Embedding with Fine-GrainedWord
Alignment, *Conf.
on Empirical Methods in Natural Language Processing* (EMNLP), 2018

D. Shen, M.R. Min, Y. Li, L. Carin, Learning
Context-Aware Convolutional Filters for Text Processing, *Conf. on Empirical Methods in Natural
Language Processing* (EMNLP), 2018

C. Tao, L. Chen, R. Zhang, R. Henao and L.
Carin, Variational
Inference and Model Selection with Generalized Evidence Bounds, Supplementary
Material, *Int.
Conf. Machine Learning* (ICML), 2018

R. Zhang, C. Chen, C. Li and L. Carin, Policy Optimization
as Wasserstein Gradient Flows, *Int. Conf. Machine Learning* (ICML), 2018

P. Chapfuwa, C. Tao, C. Li, C. Page, B.
Goldstein, L. Carin and R. Henao, Adversarial Time-to-Event
Modeling, Supplementary Material,
*Int. Conf.
Machine Learning* (ICML), 2018

C. Chen, C. Li, L. Chen, W. Wang, Y. Pu and
L, Carin, Continuous-Time
Flows for Efficient Inference and Density Estimation, Supplementary
Material, *Int. Conf. Machine Learning*
(ICML), 2018

H. Xu, L. Carin and H. Zha, Learning Registered
Point Processes from Idiosyncratic Observations, Supplementary
Material, *Int. Conf. Machine Learning*
(ICML), 2018

C. Tao, L. Chen, R. Henao, J. Feng and L.
Carin, Chi-Squared
Generative Adversarial Net, Supplementary Material,
*Int. Conf. Machine Learning* (ICML),
2018

Y. Pu, S. Dai, Z. Gan, W. Wang, G. Wang, Y.
Zhang, R. Henao and L. Carin, JointGAN:
Multi-Domain Joint Distribution Learning with Generative Adversarial Nets, Supplementary
Material, *Int. Conf. Machine Learning*
(ICML), 2018

G. Wang, C. Li, W. Wang, Y. Zhang, D. Shen,
X. Zhang, R. Henao and L. Carin, Joint Embedding of
Words and Labels for Text Classification, *Association for Computational Linguistics*
(ACL), 2018

D. Shen, G. Wang, W. Wang, M.R. Min, Q. Su,
Y. Zhang, C. Li, R. Henao and L. Carin, Baseline Needs More
Love: On Simple Word-Embedding-Based Models and Associated Pooling Mechanisms,
Supplemental
Material, *Association
for Computational Linguistics* (ACL), 2018

D. Shen, Q. Su, P. Chapfuwa, W. Wang, G.
Wang, L. Carin and R. Henao, NASH: Toward
End-to-End Neural Architecture for Generative Semantic Hashing, *Association for Computational Linguistics*
(ACL), 2018

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, *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),
*IEEE Trans. Signal Processing*, 2014

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, *IEEE Trans. Image Processing*, 2014

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), *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, *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, 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, *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,
*J. Applied Statistics, *2014

M. Zhou and L. Carin, Negative Binomial
Process Count and Mixture Modeling, *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, *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, *IEEE Trans. Biomedical Engineering*, 2010

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