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}
}
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.
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}
}
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.
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}
}