Now showing items 1-16 of 16

    • Adaptive temporal compressive sensing for video 

      Yuan, X; Yang, J; Llull, P; Liao, X; Sapiro, G; Brady, DJ; Carin, L (2013 IEEE International Conference on Image Processing, ICIP 2013 - Proceedings, 2013-12-01)
      This paper introduces the concept of adaptive temporal compressive sensing (CS) for video. We propose a CS algorithm to adapt the compression ratio based on the scene's temporal complexity, computed from the compressed data, ...
    • Augment-and-conquer negative binomial processes 

      Zhou, M; Carin, L (Advances in Neural Information Processing Systems, 2012-12-01)
      By developing data augmentation methods unique to the negative binomial (NB) distribution, we unite seemingly disjoint count and mixture models under the NB process framework. We develop fundamental properties of the models ...
    • Beta-negative binomial process and poisson factor analysis 

      Zhou, M; Hannah, LA; Dunson, DB; Carin, L (Journal of Machine Learning Research, 2012-01-01)
      © Copyright 2012 by the authors. 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 ...
    • Communications inspired linear discriminant analysis 

      Chen, M; Carson, W; Rodrigues, M; Calderbank, R; Carin, L (Proceedings of the 29th International Conference on Machine Learning, ICML 2012, 2012-10-10)
      We study the problem of supervised linear dimensionality reduction, taking an information-theoretic viewpoint. The linear projection matrix is designed by maximizing the mutual information between the projected signal and ...
    • Communications-inspired projection design with application to compressive sensing 

      Carson, WR; Chen, M; Rodrigues, MRD; Calderbank, R; Carin, L (SIAM Journal on Imaging Sciences, 2012-12-01)
      We consider the recovery of an underlying signal x ∈ ℂm based on projection measurements of the form y = Mx+w, where y ∈ ℂℓ and w is measurement noise; we are interested in the case ℓ ≪ m. It is assumed that the signal model ...
    • Cross-Domain Multitask Learning with Latent Probit Models 

      Han, S; Liao, X; Carin, L
      Learning multiple tasks across heterogeneous domains is a challenging problem since the feature space may not be the same for different tasks. We assume the data in multiple tasks are generated from a latent common domain ...
    • Dynamic nonparametric bayesian models for analysis of music 

      Ren, L; Dunson, D; Lindroth, S; Carin, L (Journal of the American Statistical Association, 2010-06-01)
      The dynamic hierarchical Dirichlet process (dHDP) is developed to model complex sequential data, with a focus on audio signals from music. The music is represented in terms of a sequence of discrete observations, and the ...
    • Generalized Bregman Divergence and Gradient of Mutual Information for Vector Poisson Channels 

    • Inferring Latent Structure From Mixed Real and Categorical Relational Data 

      Salazar, E; Cain, MS; Darling, EF; Mitroff, SR; Carin, L
      We consider analysis of relational data (a matrix), in which the rows correspond to subjects (e.g., people) and the columns correspond to attributes. The elements of the matrix may be a mix of real and categorical. Each ...
    • Latent protein trees 

      Henao, R; Thompson, JW; Moseley, MA; Ginsburg, GS; Carin, L; Lucas, JE (Annals of Applied Statistics, 2013-06-01)
      Unbiased, label-free proteomics is becoming a powerful technique for measuring protein expression in almost any biological sample. The output of these measurements after preprocessing is a collection of features and their ...
    • Learning a hybrid architecture for sequence regression and annotation 

      Zhang, Y; Henao, R; Carin, L; Zhong, J; Hartemink, AJ (30th AAAI Conference on Artificial Intelligence, AAAI 2016, 2016-01-01)
      © Copyright 2016, Association for the Advancement of Artificial Intelligence ( All rights reserved.When learning a hidden Markov model (HMM), sequential observations can often be complemented by real-valued ...
    • Lognormal and gamma mixed negative binomial regression 

      Zhou, M; Li, L; Dunson, D; Carin, L (Proceedings of the 29th International Conference on Machine Learning, ICML 2012, 2012-10-10)
      In regression analysis of counts, a lack of simple and efficient algorithms for posterior computation has made Bayesian approaches appear unattractive and thus underdeveloped. We propose a lognormal and gamma mixed negative ...
    • NASH: Toward End-to-End Neural Architecture for Generative Semantic Hashing. 

      Shen, D; Su, Q; Chapfuwa, P; Wang, W; Wang, G; Carin, L; Henao, R (CoRR, 2018)
    • Nested dictionary learning for hierarchical organization of imagery and text 

      Li, L; Zhang, XX; Zhou, M; Carin, L (Uncertainty in Artificial Intelligence - Proceedings of the 28th Conference, UAI 2012, 2012-12-01)
      A tree-based dictionary learning model is developed for joint analysis of imagery and associated text. The dictionary learning may be applied directly to the imagery from patches, or to general feature vectors extracted ...
    • Non-Gaussian discriminative factor models via the max-margin rank-likelihood 

      Yuan, X; Henao, R; Tsalik, EL; Langley, RJ; Carin, L (32nd International Conference on Machine Learning, ICML 2015, 2015-01-01)
      Copyright © 2015 by the author(s).We consider the problem of discriminative factor analysis for data that are in general non-Gaussian. A Bayesian model based on the ranks of the data is proposed. We first introduce a new ...
    • Task-driven adaptive statistical compressive sensing of gaussian mixture models 

      Duarte-Carvajalino, JM; Yu, G; Carin, L; Sapiro, G (IEEE Transactions on Signal Processing, 2013-01-21)
      A framework for adaptive and non-adaptive statistical compressive sensing is developed, where a statistical model replaces the standard sparsity model of classical compressive sensing. We propose within this framework optimal ...