2014
Martino, Luca; Elvira, Víctor; Luengo, David
An Adaptive Population Importance Sampler Proceedings Article
En: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2014), Florencia, 2014.
Enlaces | BibTeX | Etiquetas: ALCIT, COMPREHENSION
@inproceedings{Martino2014,
title = {An Adaptive Population Importance Sampler},
author = {Luca Martino and V\'{i}ctor Elvira and David Luengo},
url = {http://www.icassp2014.org/home.html},
year = {2014},
date = {2014-01-01},
booktitle = {IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2014)},
address = {Florencia},
keywords = {ALCIT, COMPREHENSION},
pubstate = {published},
tppubtype = {inproceedings}
}
Pastore, Adriano; Koch, Tobias; Fonollosa, Javier Rodriguez
A Rate-Splitting Approach to Fading Multiple-Access Channels with Imperfect Channel-State Information Proceedings Article
En: International Zurich Seminar on Communications (IZS), Zurich, 2014.
Resumen | Enlaces | BibTeX | Etiquetas: ALCIT
@inproceedings{Pastore2014,
title = {A Rate-Splitting Approach to Fading Multiple-Access Channels with Imperfect Channel-State Information},
author = {Adriano Pastore and Tobias Koch and Javier Rodriguez Fonollosa},
url = {http://www.tsc.uc3m.es/~koch/files/IZS_2014_009-012.pdf http://e-collection.library.ethz.ch/eserv/eth:8192/eth-8192-01.pdf},
year = {2014},
date = {2014-01-01},
booktitle = {International Zurich Seminar on Communications (IZS)},
address = {Zurich},
abstract = {As shown by M´edard, the capacity of fading channels with imperfect channel-state information (CSI) can be lowerbounded by assuming a Gaussian channel input and by treating the unknown portion of the channel multiplied by the channel input as independent worst-case (Gaussian) noise. Recently, we have demonstrated that this lower bound can be sharpened by a rate-splitting approach: by expressing the channel input as the sum of two independent Gaussian random variables (referred to as layers), say X = X1+X2, and by applying M´edard’s bounding technique to first lower-bound the capacity of the virtual channel from X1 to the channel output Y (while treating X2 as noise), and then lower-bound the capacity of the virtual channel from X2 to Y (while assuming X1 to be known), one obtains a lower bound that is strictly larger than M´edard’s bound. This ratesplitting approach is reminiscent of an approach used by Rimoldi and Urbanke to achieve points on the capacity region of the Gaussian multiple-access channel (MAC). Here we blend these two rate-splitting approaches to derive a novel inner bound on the capacity region of the memoryless fading MAC with imperfect CSI. Generalizing the above rate-splitting approach to more than two layers, we show that, irrespective of how we assign powers to each layer, the supremum of all rate-splitting bounds is approached as the number of layers tends to infinity, and we derive an integral expression for this supremum. We further derive an expression for the vertices of the best inner bound, maximized over the number of layers and over all power assignments.},
keywords = {ALCIT},
pubstate = {published},
tppubtype = {inproceedings}
}
Ruiz, Francisco J R; Valera, Isabel; Blanco, Carlos; Perez-Cruz, Fernando
Bayesian Nonparametric Comorbidity Analysis of Psychiatric Disorders Artículo de revista
En: Journal of Machine Learning Research, vol. 15, no 1, pp. 1215–1248, 2014.
Resumen | Enlaces | BibTeX | Etiquetas: ALCIT, Bayesian Non-parametrics, categorical observations, Indian Buet Process, Laplace approximation, multinomial-logit function, variational inference
@article{Ruiz2014,
title = {Bayesian Nonparametric Comorbidity Analysis of Psychiatric Disorders},
author = {Francisco J R Ruiz and Isabel Valera and Carlos Blanco and Fernando Perez-Cruz},
url = {http://jmlr.org/papers/volume15/ruiz14a/ruiz14a.pdf
http://arxiv.org/abs/1401.7620},
year = {2014},
date = {2014-01-01},
journal = {Journal of Machine Learning Research},
volume = {15},
number = {1},
pages = {1215--1248},
abstract = {The analysis of comorbidity is an open and complex research field in the branch of psychiatry, where clinical experience and several studies suggest that the relation among the psychiatric disorders may have etiological and treatment implications. In this paper, we are interested in applying latent feature modeling to find the latent structure behind the psychiatric disorders that can help to examine and explain the relationships among them. To this end, we use the large amount of information collected in the National Epidemiologic Survey on Alcohol and Related Conditions (NESARC) database and propose to model these data using a nonparametric latent model based on the Indian Buffet Process (IBP). Due to the discrete nature of the data, we first need to adapt the observation model for discrete random variables. We propose a generative model in which the observations are drawn from a multinomial-logit distribution given the IBP matrix. The implementation of an efficient Gibbs sampler is accomplished using the Laplace approximation, which allows integrating out the weighting factors of the multinomial-logit likelihood model. We also provide a variational inference algorithm for this model, which provides a complementary (and less expensive in terms of computational complexity) alternative to the Gibbs sampler allowing us to deal with a larger number of data. Finally, we use the model to analyze comorbidity among the psychiatric disorders diagnosed by experts from the NESARC database.},
keywords = {ALCIT, Bayesian Non-parametrics, categorical observations, Indian Buet Process, Laplace approximation, multinomial-logit function, variational inference},
pubstate = {published},
tppubtype = {article}
}
Campo, Adria Tauste; Vazquez-Vilar, Gonzalo; i Fàbregas, Albert Guillén; Koch, Tobias; Martinez, Alfonso
A Derivation of the Source-Channel Error Exponent Using Nonidentical Product Distributions Artículo de revista
En: IEEE Transactions on Information Theory, vol. 60, no 6, pp. 3209–3217, 2014, ISSN: 0018-9448.
Resumen | Enlaces | BibTeX | Etiquetas: ALCIT, Channel Coding, COMONSENS, DEIPRO, error probability, joint source-channel coding, Joints, MobileNET, Probability distribution, product distributions, random coding, Reliability, reliability function, sphere-packing bound, Upper bound
@article{TausteCampo2014,
title = {A Derivation of the Source-Channel Error Exponent Using Nonidentical Product Distributions},
author = {Adria Tauste Campo and Gonzalo Vazquez-Vilar and Albert Guill\'{e}n i F\`{a}bregas and Tobias Koch and Alfonso Martinez},
url = {http://ieeexplore.ieee.org/articleDetails.jsp?arnumber=6803047 http://www.tsc.uc3m.es/~koch/files/IEEE_TIT_60(6).pdf},
issn = {0018-9448},
year = {2014},
date = {2014-01-01},
journal = {IEEE Transactions on Information Theory},
volume = {60},
number = {6},
pages = {3209--3217},
publisher = {IEEE},
abstract = {This paper studies the random-coding exponent of joint source-channel coding for a scheme where source messages are assigned to disjoint subsets (referred to as classes), and codewords are independently generated according to a distribution that depends on the class index of the source message. For discrete memoryless systems, two optimally chosen classes and product distributions are found to be sufficient to attain the sphere-packing exponent in those cases where it is tight.},
keywords = {ALCIT, Channel Coding, COMONSENS, DEIPRO, error probability, joint source-channel coding, Joints, MobileNET, Probability distribution, product distributions, random coding, Reliability, reliability function, sphere-packing bound, Upper bound},
pubstate = {published},
tppubtype = {article}
}
Taborda, Camilo G; Guo, Dongning; Perez-Cruz, Fernando
Information--Estimation Relationships over Binomial and Negative Binomial Models Artículo de revista
En: IEEE Transactions on Information Theory, vol. to appear, pp. 1–1, 2014, ISSN: 0018-9448.
Resumen | Enlaces | BibTeX | Etiquetas: ALCIT
@article{GilTaborda2014,
title = {Information--Estimation Relationships over Binomial and Negative Binomial Models},
author = {Camilo G Taborda and Dongning Guo and Fernando Perez-Cruz},
url = {http://ieeexplore.ieee.org/articleDetails.jsp?arnumber=6746122},
issn = {0018-9448},
year = {2014},
date = {2014-01-01},
journal = {IEEE Transactions on Information Theory},
volume = {to appear},
pages = {1--1},
publisher = {IEEE},
abstract = {In recent years, a number of new connections between information measures and estimation have been found under various models, including, predominantly, Gaussian and Poisson models. This paper develops similar results for the binomial and negative binomial models. In particular, it is shown that the derivative of the relative entropy and the derivative of the mutual information for the binomial and negative binomial models can be expressed through the expectation of closed-form expressions that have conditional estimates as the main argument. Under mild conditions, those derivatives take the form of an expected Bregman divergence},
keywords = {ALCIT},
pubstate = {published},
tppubtype = {article}
}
2013
Alvarado, Alex; Brännström, Fredrik; Agrell, Erik; Koch, Tobias
On the Asymptotic Optimality of Gray Codes for BICM and One-Dimensional Constellations Proceedings Article
En: IEEE Communication Theory Workshop, Phuket, 2013.
@inproceedings{Alvarado2013a,
title = {On the Asymptotic Optimality of Gray Codes for BICM and One-Dimensional Constellations},
author = {Alex Alvarado and Fredrik Br\"{a}nnstr\"{o}m and Erik Agrell and Tobias Koch},
year = {2013},
date = {2013-01-01},
booktitle = {IEEE Communication Theory Workshop},
address = {Phuket},
keywords = {ALCIT},
pubstate = {published},
tppubtype = {inproceedings}
}
Gopalan, Prem; Ruiz, Francisco J R; Ranganath, Rajesh; Blei, David M
Bayesian Nonparametric Poisson Factorization for Recommendation Systems Proceedings Article
En: Workshop on Probabilistic Models for Big Data at Neural Information Processing Systems Conference 2013 (NIPS 2013), Lake Tahoe, 2013.
@inproceedings{Gopalan2013,
title = {Bayesian Nonparametric Poisson Factorization for Recommendation Systems},
author = {Prem Gopalan and Francisco J R Ruiz and Rajesh Ranganath and David M Blei},
year = {2013},
date = {2013-01-01},
booktitle = {Workshop on Probabilistic Models for Big Data at Neural Information Processing Systems Conference 2013 (NIPS 2013)},
address = {Lake Tahoe},
keywords = {ALCIT},
pubstate = {published},
tppubtype = {inproceedings}
}
Ruiz, Francisco J R; Valera, Isabel; Olmos, Pablo M; Blanco, Carlos; Perez-Cruz, Fernando
Infinite Continuous Feature Model for Psychiatric Comorbidity Analysis Proceedings Article
En: Workshop in Machine Learning for Clinical Data Analysis and Healthcare at Neural Information Processing Systems Conference 2013 (NIPS2013)., Lake Tahoe, 2013.
Resumen | Enlaces | BibTeX | Etiquetas: ALCIT
@inproceedings{Ruiz2013b,
title = {Infinite Continuous Feature Model for Psychiatric Comorbidity Analysis},
author = {Francisco J R Ruiz and Isabel Valera and Pablo M Olmos and Carlos Blanco and Fernando Perez-Cruz},
url = {https://googledrive.com/host/0B0TBaU3UgQ0Da3A2S2VWNTRzc1E/3.pdf},
year = {2013},
date = {2013-01-01},
booktitle = {Workshop in Machine Learning for Clinical Data Analysis and Healthcare at Neural Information Processing Systems Conference 2013 (NIPS2013).},
address = {Lake Tahoe},
abstract = {Comorbidity analysis becomes particularly relevant in the field of psychiatry, where clinical ex- perience and several studies suggest that the relation among the psychiatric disorders may have etiological and treatment implications. Several studies have focused on the search of the underlying interrelationships among psychiatric disorders, which can be useful to analyze the structure of the diagnostic classification system, and guide treatment approaches for each disorder [1]. Motivated by this relevance, in this paper we aim at finding the latent structure behind a database of psychiatric disorders. In particular, making use of the database extracted from the analysis of the National Epi- demiologic Survey on Alcohol and Related Conditions 1 (NESARC) in [1], we focus on the analysis of 20 common psychiatric disorders, including substance abuse, mood and personality disorders. Our goal is to find comorbidity patterns in the database, allowing us to seek hidden causes and to provide a tool for detecting those subjects with a high risk of suffering from these disorders.},
keywords = {ALCIT},
pubstate = {published},
tppubtype = {inproceedings}
}
Ruiz, Francisco J R; Valera, Isabel; Blanco, Carlos; Perez-Cruz, Fernando
Bayesian Nonparametric Comorbidity Analysis of Psychiatric Disorders Proceedings Article
En: 9th Conference on Bayesian Nonparametrics, Amsterdam, 2013.
@inproceedings{Ruiz2013a,
title = {Bayesian Nonparametric Comorbidity Analysis of Psychiatric Disorders},
author = {Francisco J R Ruiz and Isabel Valera and Carlos Blanco and Fernando Perez-Cruz},
year = {2013},
date = {2013-01-01},
booktitle = {9th Conference on Bayesian Nonparametrics},
address = {Amsterdam},
keywords = {ALCIT},
pubstate = {published},
tppubtype = {inproceedings}
}