@inproceedings{Nazabal2015,

title = {Discriminative spectral learning of hidden markov models for human activity recognition},

author = {Alfredo Nazabal and Antonio Art\'{e}s-Rodr\'{i}guez},

url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=7178314},

doi = {10.1109/ICASSP.2015.7178314},

isbn = {978-1-4673-6997-8},

year  = {2015},

date = {2015-04-01},

booktitle = {2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},

pages = {1966--1970},

publisher = {IEEE},

address = {Brisbane},

abstract = {Hidden Markov Models (HMMs) are one of the most important techniques to model and classify sequential data. Maximum Likelihood (ML) and (parametric and non-parametric) Bayesian estimation of the HMM parameters suffers from local maxima and in massive datasets they can be specially time consuming. In this paper, we extend the spectral learning of HMMs, a moment matching learning technique free from local maxima, to discriminative HMMs. The resulting method provides the posterior probabilities of the classes without explicitly determining the HMM parameters, and is able to deal with missing labels. We apply the method to Human Activity Recognition (HAR) using two different types of sensors: portable inertial sensors, and fixed, wireless binary sensor networks. Our algorithm outperforms the standard discriminative HMM learning in both complexity and accuracy.},

keywords = {Accuracy, Bayesian estimation, classify sequential data, Data models, Databases, Discriminative learning, discriminative spectral learning, Hidden Markov models, HMM parameters, Human activity recognition, learning (artificial intelligence), maximum likelihood, maximum likelihood estimation, ML, moment matching learning technique, Observable operator models, sensors, Spectral algorithm, spectral learning, Speech recognition, Training},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Martino2015a,

title = {Smelly Parallel MCMC Chains},

author = {Luca Martino and Victor Elvira and David Luengo and Antonio Art\'{e}s-Rodr\'{i}guez and Jukka Corander},

url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=7178736 http://www.tsc.uc3m.es/~velvira/papers/ICASSP2015_martino.pdf},

doi = {10.1109/ICASSP.2015.7178736},

isbn = {978-1-4673-6997-8},

year  = {2015},

date = {2015-04-01},

booktitle = {2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},

pages = {4070--4074},

publisher = {IEEE},

address = {Brisbane},

abstract = {Monte Carlo (MC) methods are useful tools for Bayesian inference and stochastic optimization that have been widely applied in signal processing and machine learning. A well-known class of MC methods are Markov Chain Monte Carlo (MCMC) algorithms. In this work, we introduce a novel parallel interacting MCMC scheme, where the parallel chains share information, thus yielding a faster exploration of the state space. The interaction is carried out generating a dynamic repulsion among the “smelly” parallel chains that takes into account the entire population of current states. The ergodicity of the scheme and its relationship with other sampling methods are discussed. Numerical results show the advantages of the proposed approach in terms of mean square error, robustness w.r.t. to initial values and parameter choice.},

keywords = {Bayesian inference, learning (artificial intelligence), Machine learning, Markov chain Monte Carlo, Markov chain Monte Carlo algorithms, Markov processes, MC methods, MCMC algorithms, MCMC scheme, mean square error, mean square error methods, Monte Carlo methods, optimisation, parallel and interacting chains, Probability density function, Proposals, robustness, Sampling methods, Signal processing, Signal processing algorithms, signal sampling, smelly parallel chains, smelly parallel MCMC chains, Stochastic optimization},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Perez-Cruz2015,

title = {A Blind Nonparametric Non-line of Sight Bias Model for Accurate Localization},

author = {Fernando Perez-Cruz and Howard Huang},

url = {http://ita.ucsd.edu/workshop/15/files/abstract/abstract_1462.txt},

year  = {2015},

date = {2015-02-01},

booktitle = {Information Theory and Applications (ITA)},

address = {San Diego},

abstract = {One of the most promising solutions for accurate localization services is estimating the Time Difference of Arrival (TDoA) with a cellular infrastructure and triangulating the position of the sought device. There are three different elements that limit the accuracy of TDoA: bandwidth/snr, clock accuracy and non-line-of-sight (NLOS) bias. The Cramer-Rao lower bound is well known and can be made sufficiently low (centimeters) with existing technologies. GPS clock accuracy is below 15ns (less than 5 meters). NLOS is difficult to characterize and depends heavily on the environment. We cannot rely on simple distributions to model it and we should not expect it to follow a few typical scenarios. In this talk, we present a nonparametric model for estimating the NLOS bias and an algorithm that learns the model on the fly without feedback on the true position. This procedure allows getting accurate localization in any environment and without needing to fine-tune a priori de NLOS for each base station. The actual accuracy depends on the number of bases that hear the device, but uncontrolled outliers no longer limit it. For a dense infrastructure, we show that the localization error can be measured in a few meters.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Farajtabar2014,

title = {Shaping Social Activity by Incentivizing Users},

author = {Mehrdad Farajtabar and Nan Du and Manuel Gomez-rodriguez and Isabel Valera and Hongyuan Zha and Le Song},

url = {http://papers.nips.cc/paper/5365-shaping-social-activity-by-incentivizing-users.pdf},

year  = {2014},

date = {2014-12-01},

booktitle = {Advances in Neural Information Processing Systems},

volume = {December},

pages = {2474--2482},

address = {Montreal},

abstract = {Events in an online social network can be categorized roughly into endogenous events, where users just respond to the actions of their neighbors within the network, or exogenous events, where users take actions due to drives external to the network. How much external drive should be provided to each user, such that the network activity can be steered towards a target state? In this paper, we model social events using multivariate Hawkes processes, which can capture both endogenous and exogenous event intensities, and derive a time dependent linear relation between the intensity of exogenous events and the overall network activity. Exploiting this connection, we develop a convex optimization framework for determining the required level of external drive in order for the network to reach a desired activity level. We experimented with event data gathered from Twitter, and show that our method can steer the activity of the network more accurately than alternatives.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{gvazquez-isit2014,

title = {Source-Channel Coding with Multiple Classes},

author = {Irina E Bocharova and Albert Guill\'{e}n i F\`{a}bregas and Boris D Kudryashov and Alfonso Martinez and Adria Tauste Campo and Gonzalo Vazquez-Vilar},

year  = {2014},

date = {2014-06-01},

booktitle = {2014 IEEE International Symposium on Information Theory (ISIT 2014)},

address = {Honolulu, HI, USA},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Taborda2014,

title = {New Information-Estimation Results for Poisson, Binomial and Negative Binomial Models},

author = {Camilo G Taborda and Fernando Perez-Cruz and Dongning Guo},

url = {http://ieeexplore.ieee.org/articleDetails.jsp?arnumber=6875225},

doi = {10.1109/ISIT.2014.6875225},

isbn = {978-1-4799-5186-4},

year  = {2014},

date = {2014-06-01},

booktitle = {2014 IEEE International Symposium on Information Theory},

pages = {2207--2211},

publisher = {IEEE},

address = {Honolulu},

abstract = {In recent years, a number of mathematical relationships have been established between information measures and estimation measures for various models, including Gaussian, Poisson and binomial models. In this paper, it is shown that the second derivative of the input-output mutual information with respect to the input scaling can be expressed as the expectation of a certain Bregman divergence pertaining to the conditional expectations of the input and the input power. This result is similar to that found for the Gaussian model where the Bregman divergence therein is the square distance. In addition, the Poisson, binomial and negative binomial models are shown to be similar in the small scaling regime in the sense that the derivative of the mutual information and the derivative of the relative entropy converge to the same value.},

keywords = {Bregman divergence, Estimation, estimation measures, Gaussian models, Gaussian processes, information measures, information theory, information-estimation results, negative binomial models, Poisson models, Stochastic processes},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Miguez2014,

title = {On the uniform asymptotic convergence of a distributed particle filter},

author = {Joaquin Miguez},

url = {http://ieeexplore.ieee.org/articleDetails.jsp?arnumber=6882385},

doi = {10.1109/SAM.2014.6882385},

isbn = {978-1-4799-1481-4},

year  = {2014},

date = {2014-06-01},

booktitle = {2014 IEEE 8th Sensor Array and Multichannel Signal Processing Workshop (SAM)},

pages = {241--244},

publisher = {IEEE},

address = {A Coru\~{n}a},

abstract = {Distributed signal processing algorithms suitable for their implementation over wireless sensor networks (WSNs) and ad hoc networks with communications and computing capabilities have become a hot topic during the past years. One class of algorithms that have received special attention are particles filters. However, most distributed versions of this type of methods involve various heuristic or simplifying approximations and, as a consequence, classical convergence theorems for standard particle filters do not hold for their distributed counterparts. In this paper, we look into a distributed particle filter scheme that has been proposed for implementation in both parallel computing systems and WSNs, and prove that, under certain stability assumptions regarding the physical system of interest, its asymptotic convergence is guaranteed. Moreover, we show that convergence is attained uniformly over time. This means that approximation errors can be kept bounded for an arbitrarily long period of time without having to progressively increase the computational effort.},

keywords = {ad hoc networks, Approximation algorithms, approximation errors, Approximation methods, classical convergence theorems, Convergence, convergence of numerical methods, distributed particle filter scheme, distributed signal processing algorithms, Monte Carlo methods, parallel computing systems, particle filtering (numerical methods), Signal processing, Signal processing algorithms, stability assumptions, uniform asymptotic convergence, Wireless Sensor Networks, WSNs},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Crisan2014b,

title = {Nested Particle Filters for Sequential Parameter Estimation in Discrete-time State-space Models},

author = {Dan Crisan and Joaquin Miguez},

year  = {2014},

date = {2014-03-01},

booktitle = {SIAM 2014 Conference on Uncertainty Quantification},

address = {Savannah},

abstract = {The problem of estimating the parameters of nonlinear, possibly non-Gaussian discrete-time state models has drawn considerable attention during the past few years, leading to the appearance of general methodologies (SMC2, particle MCMC, recursive ML) that have improved on earlier, simpler extensions of the standard particle filter. However, there is still a lack of recursive (online) methods that can provide a theoretically-grounded approximation of the joint posterior probability distribution of the parameters and the dynamic state variables of the model. In the talk, we will describe a two-layer particle filtering scheme that addresses this problem. Both a recursive algorithm, suitable for online implementation, and some results regarding its asymptotic convergence will be presented.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@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}

}

@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}

}

@inproceedings{Montoya-Martinez2014,

title = {Structured Sparse-Low Rank Matrix Factorization for the EEG Inverse Problem},

author = {Jair Montoya-Martinez and Antonio Art\'{e}s-Rodr\'{i}guez and Massimiliano Pontil},

url = {http://www.conwiz.dk/cgi-all/cip2014/view_abstract.pl?idno=21},

year  = {2014},

date = {2014-01-01},

booktitle = {4th International Workshop on Cognitive Information Processing (CIP 2014)},

address = {Copenhagen},

abstract = {We consider the estimation of the Brain Electrical Sources (BES) matrix from noisy EEG measurements, commonly named as the EEG inverse problem. We propose a new method based on the factorization of the BES as a product of a sparse coding matrix and a dense latent source matrix. This structure is enforced by minimizing a regularized functional that includes the $backslash ell_21$-norm of the coding matrix and the squared Frobenius norm of the latent source matrix. We develop an alternating optimization algorithm to solve the resulting nonsmooth-nonconvex minimization problem. We have evaluated our approach under a simulated scenario consisting on estimating a synthetic BES matrix with 5124 sources. We compare the performance of our method respect to the Lasso, Group Lasso, Sparse Group Lasso and Trace norm regularizers.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Elvira2014,

title = {A Novel Feature Extraction Technique for Human Activity Recognition},

author = {V\'{i}ctor Elvira and Alfredo Nazabal and Antonio Art\'{e}s-Rodr\'{i}guez},

url = {http://edas.info/p15153#S1569490857},

year  = {2014},

date = {2014-01-01},

booktitle = {2014 IEEE Workshop on Statistical Signal Processing (SSP 2014)},

address = {Gold Coast},

abstract = {This work presents a novel feature extraction technique for human activity recognition using inertial and magnetic sensors. The proposed method estimates the orientation of the person with respect to the earth frame by using quaternion representation. This estimation is performed automatically without any extra information about where the sensor is placed on the body of the person. Furthermore, the method is also robust to displacements of the sensor with respect to the body. This novel feature extraction technique is used to feed a classification algorithm showing excellent results that outperform those obtained by an existing state-of-the-art feature extraction technique.},

keywords = {Activity Classification, Ambulatory Monitoring, Features Extraction, Inertial sensors, Magnetic, orientation estimation, Quaternions., sensors},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Martino2014b,

title = {Orthogonal MCMC Algorithms},

author = {Luca Martino and V\'{i}ctor Elvira and David Luengo and Antonio Art\'{e}s-Rodr\'{i}guez and Jukka Corander},

url = {http://edas.info/p15153#S1569490857},

year  = {2014},

date = {2014-01-01},

booktitle = {2014 IEEE Workshop on Statistical Signal Processing (SSP 2014)},

address = {Gold Coast},

abstract = {Monte Carlo (MC) methods are widely used in signal processing, machine learning and stochastic optimization. A wellknown class of MC methods are Markov Chain Monte Carlo (MCMC) algorithms. In this work, we introduce a novel parallel interacting MCMC scheme, where the parallel chains share information using another MCMC technique working on the entire population of current states. These parallel “vertical” chains are led by random-walk proposals, whereas the “horizontal” MCMC uses a independent proposal, which can be easily adapted by making use of all the generated samples. Numerical results show the advantages of the proposed sampling scheme in terms of mean absolute error, as well as robustness w.r.t. to initial values and parameter choice.},

keywords = {Bayesian inference, Markov Chain Monte Carlo (MCMC), Parallel Chains, population Monte Carlo},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Trigano2014,

title = {Grouped Sparsity Algorithm for Multichannel Intracardiac ECG Synchronization},

author = {Tom Trigano and V Kolesnikov and David Luengo and Antonio Art\'{e}s-Rodr\'{i}guez},

year  = {2014},

date = {2014-01-01},

booktitle = {22nd European Signal Processing Conference (EUSIPCO 2014)},

address = {Lisbon},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Pradier2014,

title = {Map/Reduce Uncollapsed Gibbs Sampling for Bayesian Non Parametric Models},

author = {Melanie F. Pradier and Pablo Garcia-Moreno and Francisco J R Ruiz and Isabel Valera and Harold Molina-Bulla and Fernando Perez-Cruz},

year  = {2014},

date = {2014-01-01},

booktitle = {NIPS Workshop on Software Engineering for Machine Learning},

address = {Montreal},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Ruiz2014b,

title = {True Natural Gradient of Collapsed Variational Bayes},

author = {Francisco J R Ruiz and Neil D Lawrence and James Hensman},

year  = {2014},

date = {2014-01-01},

booktitle = {NIPS Workshop on Advances in Variational Inference},

address = {Montreal},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Valera2014a,

title = {Infinite Factorial Unbounded Hidden Markov Model for Blind Multiuser Channel Estimation},

author = {Isabel Valera and Francisco J R Ruiz and Fernando Perez-Cruz},

url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6844506},

isbn = {978-1-4799-3696-0},

year  = {2014},

date = {2014-01-01},

booktitle = {2014 4th International Workshop on Cognitive Information Processing (CIP)},

pages = {1--6},

publisher = {IEEE},

address = {Copenhagen},

abstract = {Bayesian nonparametric models allow solving estimation and detection problems with an unbounded number of degrees of freedom. In multiuser multiple-input multiple-output (MIMO) communication systems we might not know the number of active users and the channel they face, and assuming maximal scenarios (maximum number of transmitters and maximum channel length) might degrade the receiver performance. In this paper, we propose a Bayesian nonparametric prior and its associated inference algorithm, which is able to detect an unbounded number of users with an unbounded channel length. This generative model provides the dispersive channel model for each user and a probabilistic estimate for each transmitted symbol in a fully blind manner, i.e., without the need of pilot (training) symbols.},

keywords = {Bayes methods, Bayesian non parametrics, Bayesian nonparametric models, blind multiuser channel estimation, Channel estimation, degrees of freedom, detection problems, dispersive channel model, generative model, Hidden Markov models, HMM, inference algorithm, infinite factorial unbounded hidden Markov model, Markov chain Monte Carlo, Markov processes, MIMO, MIMO communication, MIMO communication systems, multiple-input multiple-output (MIMO), multiple-input multiple-output communication syste, receiver performance, Receivers, Signal to noise ratio, Transmitters, unbounded channel length, unbounded number, user detection},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Gopalan2014,

title = {Bayesian Nonparametric Poisson Factorization for Recommendation Systems},

author = {Prem Gopalan and Francisco J R Ruiz and Rajesh Ranganath and David M Blei},

year  = {2014},

date = {2014-01-01},

booktitle = {International Conference on Artificial Intelligence and Statistics (AISTATS)},

address = {Reykjavik},

abstract = {We develop a Bayesian nonparametric Poisson factorization model for recommendation systems. Poisson factorization implicitly models each user's limited budget of attention (or money) that allows consumption of only a small subset of the available items. In our Bayesian nonparametric variant, the number of latent components is theoretically unbounded and e ectively estimated when computing a posterior with observed user behavior data. To approximate the posterior, we develop an ecient variational inference algorithm. It adapts the dimensionality of the latent components to the data, only requires iteration over the user/item pairs that have been rated, and has computational complexity on the same order as for a parametric model with xed dimensionality. We studied our model and algorithm with large realworld data sets of user-movie preferences. Our model eases the computational burden of searching for the number of latent components and gives better predictive performance than its parametric counterpart.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Yang2014b,

title = {Dispersion of Quasi-Static MIMO Fading Channels via Stokes' Theorem},

author = {Wei Yang and Giuseppe Durisi and Tobias Koch and Yury Polyanskiy},

url = {http://ieeexplore.ieee.org/articleDetails.jsp?arnumber=6875198},

isbn = {978-1-4799-5186-4},

year  = {2014},

date = {2014-01-01},

booktitle = {2014 IEEE International Symposium on Information Theory},

pages = {2072--2076},

publisher = {IEEE},

address = {Honolulu},

abstract = {This paper analyzes the channel dispersion of quasi-static multiple-input multiple-output fading channels with no channel state information at the transmitter. We show that the channel dispersion is zero under mild conditions on the fading distribution. The proof of our result is based on Stokes' theorem, which deals with the integration of differential forms on manifolds with boundary.},

keywords = {channel capacity, differential form integration, Dispersion, Fading, fading channels, fading distribution, integration, Manifolds, Measurement, MIMO, MIMO communication, quasistatic MIMO fading channels dispersion, quasistatic multiple-input multiple-output fading, radio transmitters, Random variables, Stoke Theorem, transmitter},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Koch2014,

title = {On the Dither-Quantized Gaussian Channel at Low SNR},

author = {Tobias Koch},

url = {http://ieeexplore.ieee.org/articleDetails.jsp?arnumber=6874820},

isbn = {978-1-4799-5186-4},

year  = {2014},

date = {2014-01-01},

booktitle = {2014 IEEE International Symposium on Information Theory},

pages = {186--190},

publisher = {IEEE},

address = {Honolulu},

abstract = {We study the capacity of the peak-and-average-power-limited Gaussian channel when its output is quantized using a dithered, infinite-level, uniform quantizer of step size $Delta$. We focus on the low signal-to-noise-ratio (SNR) regime, where communication at low spectral efficiencies takes place. We show that, when the peak-power constraint is absent, the low-SNR asymptotic capacity is equal to that of the unquantized channel irrespective of $Delta$. We further derive an expression for the low-SNR asymptotic capacity for finite peak-to-average-power ratios and evaluate it in the low- and high-resolution limit. We demonstrate that, in this case, the low-SNR asymptotic capacity converges to that of the unquantized channel when $Delta$ tends to zero, and it tends to zero when $Delta$ tends to infinity.},

keywords = {Additive noise, channel capacity, dither quantized Gaussian channel, Entropy, Gaussian channels, low signal-to-noise-ratio, low-SNR asymptotic capacity, peak power constraint, peak-and-average-power-limited Gaussian channel, Quantization (signal), Signal to noise ratio},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Ostman2014,

title = {Diversity Versus Multiplexing at Finite Blocklength},

author = {Johan Ostman and Wei Yang and Giuseppe Durisi and Tobias Koch},

url = {http://ieeexplore.ieee.org/articleDetails.jsp?arnumber=6933444},

isbn = {978-1-4799-5863-4},

year  = {2014},

date = {2014-01-01},

booktitle = {2014 11th International Symposium on Wireless Communications Systems (ISWCS)},

pages = {702--706},

publisher = {IEEE},

address = {Barcelona},

abstract = {A finite blocklenth analysis of the diversity-multiplexing tradeoff is presented, based on nonasymptotic bounds on the maximum channel coding rate of multiple-antenna block-memoryless Rayleigh-fading channels. The bounds in this paper allow one to numerically assess for which packet size, number of antennas, and degree of channel selectivity, diversity-exploiting schemes are close to optimal, and when instead the available spatial degrees of freedom should be used to provide spatial multiplexing. This finite blocklength view on the diversity-multiplexing tradeoff provides insights on the design of delay-sensitive ultra-reliable communication links.},

keywords = {Antennas, Channel Coding, channel selectivity, Coherence, delay-sensitive ultra-reliable communication links, diversity reception, diversity-exploiting schemes, diversity-multiplexing tradeoff, Fading, finite blocklength analysis, maximum channel coding rate, multiple-antenna block-memoryless Rayleigh-fading, Multiplexing, nonasymptotic bounds, packet size, radio links, Rayleigh channels, Time-frequency analysis, Transmitters, Upper bound},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Cespedes2014b,

title = {Improved Performance of LDPC-Coded MIMO Systems with EP-based Soft-Decisions},

author = {Javier Cespedes and Pablo M Olmos and Matilde Sanchez-Fernandez and Fernando Perez-Cruz},

url = {http://ieeexplore.ieee.org/articleDetails.jsp?arnumber=6875183},

isbn = {978-1-4799-5186-4},

year  = {2014},

date = {2014-01-01},

booktitle = {2014 IEEE International Symposium on Information Theory},

pages = {1997--2001},

publisher = {IEEE},

address = {Honolulu},

abstract = {Modern communications systems use efficient encoding schemes, multiple-input multiple-output (MIMO) and high-order QAM constellations for maximizing spectral efficiency. However, as the dimensions of the system grow, the design of efficient and low-complexity MIMO receivers possesses technical challenges. Symbol detection can no longer rely on conventional approaches for posterior probability computation due to complexity. Marginalization of this posterior to obtain per-antenna soft-bit probabilities to be fed to a channel decoder is computationally challenging when realistic signaling is used. In this work, we propose to use Expectation Propagation (EP) algorithm to provide an accurate low-complexity Gaussian approximation to the posterior, easily solving the posterior marginalization problem. EP soft-bit probabilities are used in an LDPC-coded MIMO system, achieving outstanding performance improvement compared to similar approaches in the literature for low-complexity LDPC MIMO decoding.},

keywords = {Approximation algorithms, Approximation methods, approximation theory, Channel Coding, channel decoder, communication complexity, complexity, Complexity theory, Detectors, encoding scheme, EP soft bit probability, EP-based soft decision, error statistics, expectation propagation, expectation-maximisation algorithm, expectation-propagation algorithm, Gaussian approximation, Gaussian channels, LDPC, LDPC coded MIMO system, Low Complexity receiver, MIMO, MIMO communication, MIMO communication systems, MIMO receiver, modern communication system, multiple input multiple output, parity check codes, per-antenna soft bit probability, posterior marginalization problem, posterior probability computation, QAM constellation, Quadrature amplitude modulation, radio receivers, signaling, spectral analysis, spectral efficiency maximization, symbol detection, telecommunication signalling, Vectors},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Stinner2014,

title = {Analyzing Finite-length Protograph-Based Spatially Coupled LDPC Codes},

author = {Markus Stinner and Pablo M Olmos},

url = {http://ieeexplore.ieee.org/articleDetails.jsp?arnumber=6874961},

isbn = {978-1-4799-5186-4},

year  = {2014},

date = {2014-01-01},

booktitle = {2014 IEEE International Symposium on Information Theory},

pages = {891--895},

publisher = {IEEE},

address = {Honolulu},

abstract = {The peeling decoding for spatially coupled low-density parity-check (SC-LDPC) codes is analyzed for a binary erasure channel. An analytical calculation of the mean evolution of degree-one check nodes of protograph-based SC-LDPC codes is given and an estimate for the covariance evolution of degree-one check nodes is proposed in the stable decoding phase where the decoding wave propagates along the chain of coupled codes. Both results are verified numerically. Protograph-based SC-LDPC codes turn out to have a more robust behavior than unstructured random SC-LDPC codes. Using the analytically calculated parameters, the finite-length scaling laws for these constructions are given and verified by numerical simulations.},

keywords = {binary erasure channel, covariance analysis, covariance evolution, Decoding, degree-one check nodes, Error analysis, finite-length protograph, mean evolution, Monte Carlo methods, parity check codes, peeling decoding, protograph-based SC-LDPC codes, spatially coupled low-density parity-check codes, stable decoding phase, Steady-state, Vectors},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Olmos2014,

title = {Improving the Finite-Length Performance of Long SC-LDPC Code Chains by Connecting Consecutive Chains},

author = {Pablo M Olmos and David G M Mitchell and Dimitri Truhachev and Daniel J Costello},

url = {http://ieeexplore.ieee.org/articleDetails.jsp?arnumber=6955088},

year  = {2014},

date = {2014-01-01},

booktitle = {8th IEEE International Symposium on Turbo Codes \& Iterative Information Processing},

pages = {72--76},

publisher = {IEEE},

address = {Bremen},

abstract = {We propose a novel encoding/transmission scheme called continuous chain (CC) transmission that is able to improve the finite-length performance of a system using long spatially coupled low-density parity-check (SC-LDPC) code chains. First, we show that the decoding of SC-LDPC code chains is more reliable for shorter chain lengths, i.e., the scaling between block error rate and gap to threshold is more favorable for shorter chains. This motivates the use of CC transmission in which, instead of transmitting a sequence of independent codewords from a long SC-LDPC chain, we connect multiple chains in a layered format, where encoding, transmission, and decoding are now performed in a continuous fashion. Finally, we show that CC transmission can be implemented with only a small increase in decoding complexity or delay with respect to a system employing a single SC-LDPC code chain for transmission},

keywords = {Decoding, Error analysis, error probability, Information processing, parity check codes, Turbo codes},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Djuric2014,

title = {Cooperative Mesh Networks with EGC Detectors},

author = {Petar M Djuric and \'{A}ngel M Bravo-Santos},

url = {http://ieeexplore.ieee.org/articleDetails.jsp?arnumber=6882381},

isbn = {978-1-4799-1481-4},

year  = {2014},

date = {2014-01-01},

booktitle = {2014 IEEE 8th Sensor Array and Multichannel Signal Processing Workshop (SAM)},

pages = {225--228},

publisher = {IEEE},

address = {A Coru\~{n}a},

abstract = {We address mesh networks with decode and forward relays that use binary modulations. For detection, the nodes employ equal gain combining, which is appealing because it is very easy to implement. We study the performance of these networks and compare it to that of multihop multi-branch networks. We also examine the performance of the networks when both the number of groups and total number of nodes are fixed but the topology of the network varies. We demonstrate the performance of these networks where the channels are modeled with Nakagami distributions and the noise is zero mean Gaussian},

keywords = {binary modulations, cooperative communications, cooperative mesh networks, decode and forward communication, decode and forward relays, Detectors, EGC detectors, Gaussian processes, Joints, Manganese, Mesh networks, multihop multibranch networks, Nakagami channels, Nakagami distribution, Nakagami distributions, relay networks (telecommunication), Signal to noise ratio, zero mean Gaussian},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Valera2014b,

title = {General Table Completion using a Bayesian Nonparametric Model},

author = {Isabel Valera and Zoubin Ghahramani},

year  = {2014},

date = {2014-01-01},

booktitle = {Neural Information Processing Systems Conference 2014 (NIPS 2014)},

address = {Montreal},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{gvazquez-isit2013,

title = {The Meta-Converse Bound is Tight},

author = {Gonzalo Vazquez-Vilar and Adria Tauste Campo and Albert Guill\'{e}n i F\`{a}bregas and Alfonso Martinez},

year  = {2013},

date = {2013-07-01},

booktitle = {2013 IEEE International Symposium on Information Theory (ISIT 2013)},

address = {Istanbul, Turkey},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Alvarado2013b,

title = {High-SNR Asymptotics of Mutual Information for Discrete Constellations},

author = {Alex Alvarado and Fredrik Brannstrom and Erik Agrell and Tobias Koch},

url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6620631},

issn = {2157-8095},

year  = {2013},

date = {2013-01-01},

booktitle = {2013 IEEE International Symposium on Information Theory},

pages = {2274--2278},

publisher = {IEEE},

address = {Istanbul},

abstract = {The asymptotic behavior of the mutual information (MI) at high signal-to-noise ratio (SNR) for discrete constellations over the scalar additive white Gaussian noise channel is studied. Exact asymptotic expressions for the MI for arbitrary one-dimensional constellations and input distributions are presented in the limit as the SNR tends to infinity. Asymptotics of the minimum mean-square error (MMSE) are also developed. It is shown that for any input distribution, the MI and the MMSE have an asymptotic behavior proportional to a Gaussian Q-function, whose argument depends on the minimum Euclidean distance of the constellation and the SNR. Closed-form expressions for the coefficients of these Q-functions are calculated.},

keywords = {AWGN channels, discrete constellations, Entropy, Fading, Gaussian Q-function, high-SNR asymptotics, IP networks, least mean squares methods, minimum mean-square error, MMSE, Mutual information, scalar additive white Gaussian noise channel, Signal to noise ratio, signal-to-noise ratio, Upper bound},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Koblents2013,

title = {Robust Mixture Population Monte Carlo Scheme with Adaptation of the Number of Components},

author = {Eugenia Koblents and Joaquin Miguez},

url = {http://www.eusipco2013.org/},

year  = {2013},

date = {2013-01-01},

booktitle = {European Signal Processing Conference (EUSIPCO) 2013},

address = {Marrakech},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Salamanca2013,

title = {Improving the BP Estimate over the AWGN Channel Using Tree-Structured Expectation Propagation},

author = {Luis Salamanca and Juan Jose Murillo-Fuentes and Pablo M Olmos and Fernando Perez-Cruz},

url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6620774},

issn = {2157-8095},

year  = {2013},

date = {2013-01-01},

booktitle = {2013 IEEE International Symposium on Information Theory},

pages = {2990--2994},

publisher = {IEEE},

address = {Istanbul},

abstract = {In this paper, we propose the tree-structured expectation propagation (TEP) algorithm for low-density parity-check (LDPC) decoding over the binary additive white Gaussian noise (BI-AWGN) channel. By approximating the posterior distribution by a tree-structure factorization, the TEP has been proven to improve belief propagation (BP) decoding over the binary erasure channel (BEC). We show for the AWGN channel how the TEP decoder is also able to capture additional information disregarded by the BP solution, which leads to a noticeable reduction of the error rate for finite-length codes. We show that for the range of codes of interest, the TEP gain is obtained with a slight increase in complexity over that of the BP algorithm. An efficient way of constructing the tree-like structure is also described.},

keywords = {Approximation algorithms, Approximation methods, AWGN channels, BEC, belief propagation decoding, BI-AWGN channel, binary additive white Gaussian noise channel, binary erasure channel, BP estimation, Channel Coding, Complexity theory, error rate reduction, error statistics, Expectation, finite-length codes, Iterative decoding, LDPC codes, LDPC decoding, low-density parity-check decoding, Maximum likelihood decoding, parity check codes, posterior distribution, Propagation, TEP algorithm, tree-structured expectation propagation algorithm, trees (mathematics)},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Yang2013,

title = {Block-Fading Channels at Finite Blocklength},

author = {Wei Yang and Giuseppe Durisi and Tobias Koch and Yury Polyanskiy},

url = {http://publications.lib.chalmers.se/publication/185700},

year  = {2013},

date = {2013-01-01},

booktitle = {Proceedings of the International Symposium on Wireless Communication Systems (ISWCS), Ilmenau, Germany, Aug. 2013},

address = {Ilmenau},

abstract = {This tutorial paper deals with the problem of characterizing the maximal achievable rate R* (n,$epsilon$) at a given blocklength n; and error probability $epsilon$ over block-fading channels. We review recent results that establish tight bounds on R* (n ,$epsilon$) and characterize its asymptotic behavior. Comparison between the theoretical results and the data rates achievable with the coding scheme used in LTE-Advanced are reported.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Bifet2013,

title = {Machine Learning and Knowledge Discovery in Databases},

author = {Albert Bifet and Jesse Read and Indre Zliobaite and Bernhard Pfahringer and Geoff Holmes},

editor = {Hendrik Blockeel and Kristian Kersting and Siegfried Nijssen and Filip \v{Z}elezn\'{y}},

url = {http://link.springer.com/10.1007/978-3-642-40988-2},

isbn = {978-3-642-40987-5},

year  = {2013},

date = {2013-01-01},

booktitle = {ECML 2013: 24th European Conference on Machine Learning},

volume = {8188},

publisher = {Springer Berlin Heidelberg},

series = {Lecture Notes in Computer Science},

abstract = {Data stream classification plays an important role in modern data analysis, where data arrives in a stream and needs to be mined in real time. In the data stream setting the underlying distribution from which this data comes may be changing and evolving, and so classifiers that can update themselves during operation are becoming the state-of-the-art. In this paper we show that data streams may have an important temporal component, which currently is not considered in the evaluation and benchmarking of data stream classifiers. We demonstrate how a naive classifier considering the temporal component only outperforms a lot of current state-of-the-art classifiers on real data streams that have temporal dependence, i.e. data is autocorrelated. We propose to evaluate data stream classifiers taking into account temporal dependence, and introduce a new evaluation measure, which provides a more accurate gauge of data stream classifier performance. In response to the temporal dependence issue we propose a generic wrapper for data stream classifiers, which incorporates the temporal component into the attribute space.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Ruiz2013,

title = {A Bayesian Nonparametric Receiver for Joint Channel Estimation and Symbol Detection for Multiple Users},

author = {Francisco J R Ruiz and Isabel Valera and Fernando Perez-Cruz},

url = {http://ita.ucsd.edu/workshop/13/talks},

year  = {2013},

date = {2013-01-01},

booktitle = {Information Theory and Applications (ITA)},

address = {San Diego},

abstract = {Bayesian nonparametric models allow solving estimation and detection problems with an unbounded number of degrees of freedom. In multi-user environments we might not know the number of active users and the channel they face and assuming maximal scenarios (maximum number of users and dispersive channels) might degrade the receiver performance. In this presentation, we propose a Bayesian nonparametric prior that it is able to detect an unbounded number of users with an unbounded channel delay. This generative model provides the dispersive channel model for each user and a probabilistic estimate for each received symbol without a preamble.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Durisi2013,

title = {On the Multiplexing Gain of MIMO Microwave Backhaul Links Affected by Phase Noise},

author = {Giuseppe Durisi and Alberto Tarable and Tobias Koch},

url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6655038},

issn = {1550-3607},

year  = {2013},

date = {2013-01-01},

booktitle = {2013 IEEE International Conference on Communications (ICC)},

pages = {3209--3214},

publisher = {IEEE},

address = {Budapest},

abstract = {We consider a multiple-input multiple-output (MIMO) AWGN channel affected by phase noise. Focusing on the 2 × 2 case, we show that no MIMO multiplexing gain is to be expected when the phase-noise processes at each antenna are independent, memoryless in time, and with uniform marginal distribution over [0, 2$pi$] (strong phase noise), and when the transmit signal is isotropically distributed on the real plane. The scenario of independent phase-noise processes across antennas is relevant for microwave backhaul links operating in the 20-40 GHz range.},

keywords = {AWGN channels, marginal distribution, Microwave antennas, microwave links, MIMO, MIMO AWGN channel, MIMO communication, MIMO microwave backhaul links, MIMO multiplexing gain, multiple-input multiple-output AWGN channel, Multiplexing, Phase noise, phase-noise processes, Receivers, Signal to noise ratio, strong phase noise, transmit signal, Transmitters},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Read2013,

title = {Eficient Monte Carlo Optimization for Multi-Label Classifier Chains},

author = {Jesse Read and Luca Martino and David Luengo},

year  = {2013},

date = {2013-01-01},

booktitle = {ICASSP 2013: The 38th International Conference on Acoustics, Speech, and Signal Processing},

address = {Vancouver},

abstract = {Multi-dimensional classification (MDC) is the supervised learning problem where an instance is associated with multiple classes, rather than with a single class, as in traditional classification problems. Since these classes are often strongly correlated, modeling the dependencies between them allows MDC methods to improve their performance at the expense of an increased computational cost. In this paper we focus on the classifier chains (CC) approach for modeling dependencies, one of the most popular and highest- performing methods for multi-label classification (MLC), a particular case of MDC which involves only binary classes (i.e., labels). The original CC algorithm makes a greedy approximation, and is fast but tends to propagate errors along the chain. Here we present novel Monte Carlo schemes, both for nding a good chain sequence and performing ecient inference. Our algorithms remain tractable for high-dimensional data sets and obtain the best predictive performance across several real data sets.},

keywords = {Bayesian inference, Classifier chains, Monte Carlo methods, Multi-dimensional classification, Multi-label classification},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Luengo2013,

title = {Cross-Products LASSO},

author = {David Luengo and Javier Via and Sandra Monzon and Tom Trigano and Antonio Art\'{e}s-Rodr\'{i}guez},

url = {http://ieeexplore.ieee.org/articleDetails.jsp?arnumber=6638840},

issn = {1520-6149},

year  = {2013},

date = {2013-01-01},

booktitle = {2013 IEEE International Conference on Acoustics, Speech and Signal Processing},

pages = {6118--6122},

publisher = {IEEE},

address = {Vancouver},

abstract = {Negative co-occurrence is a common phenomenon in many signal processing applications. In some cases the signals involved are sparse, and this information can be exploited to recover them. In this paper, we present a sparse learning approach that explicitly takes into account negative co-occurrence. This is achieved by adding a novel penalty term to the LASSO cost function based on the cross-products between the reconstruction coefficients. Although the resulting optimization problem is non-convex, we develop a new and efficient method for solving it based on successive convex approximations. Results on synthetic data, for both complete and overcomplete dictionaries, are provided to validate the proposed approach.},

keywords = {Approximation methods, approximation theory, concave programming, convex programming, Cost function, cross-product LASSO cost function, Dictionaries, dictionary, Encoding, LASSO, learning (artificial intelligence), negative co-occurrence, negative cooccurrence phenomenon, nonconvex optimization problem, Signal processing, signal processing application, signal reconstruction, sparse coding, sparse learning approach, Sparse matrices, sparsity-aware learning, successive convex approximation, Vectors},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Olmos2013a,

title = {A Closed-Form Scaling Law for Convolutional LDPC Codes Over the BEC},

author = {Pablo M Olmos and Rudiger Urbanke},

url = {http://itw2013.tsc.uc3m.es/authors},

year  = {2013},

date = {2013-01-01},

booktitle = {2013 IEEE Information Theory Workshop},

address = {Seville},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Vazquez2013,

title = {Automated Detection of Paroxysmal Gamma Waves in Meditation EEG},

author = {Manuel A Vazquez and Jing Jin and Justin Dauwels and Francois B Vialatte},

url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6637839},

issn = {1520-6149},

year  = {2013},

date = {2013-01-01},

booktitle = {2013 IEEE International Conference on Acoustics, Speech and Signal Processing},

pages = {1192--1196},

publisher = {IEEE},

address = {Vancouver},

abstract = {Meditation is a fascinating topic, yet has received limited attention in the neuroscience and signal processing community so far. A few studies have investigated electroencephalograms (EEG) recorded during meditation. Strong EEG activity has been observed in the left temporal lobe of meditators. Meditators exhibit more paroxysmal gamma waves (PGWs) in active regions of the brain. In this paper, a method is proposed to automatically detect PGWs from meditation EEG. The proposed algorithm is able to identify multiple sources in the brain that generate PGWs, and the sources associated with different types of PGWs can be distinguished. The effectiveness of the proposed method is assessed on 3 subjects possessing different degrees of expertise in practicing a yoga type meditation known as Bhramari Pranayama.},

keywords = {automated detection, Bhramari Pranayama, Blind source separation, brain active region, brain multiple source identification, Detectors, EEG activity, Electroencephalogram, Electroencephalography, left temporal lobe, medical signal detection, Meditation, meditation EEG, meditator, neurophysiology, neuroscience, Paroxysmal gamma wave, paroxysmal gamma waves, PGW, Principal component analysis, Sensitivity, signal processing community, Spike detection, Temporal lobe, yoga type meditation},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Yang2013a,

title = {Quasi-Static SIMO Fading Channels at Finite Blocklength},

author = {Wei Yang and Giuseppe Durisi and Tobias Koch and Yury Polyanskiy},

url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6620483},

issn = {2157-8095},

year  = {2013},

date = {2013-01-01},

booktitle = {2013 IEEE International Symposium on Information Theory},

pages = {1531--1535},

publisher = {IEEE},

address = {Istanbul},

abstract = {We investigate the maximal achievable rate for a given blocklength and error probability over quasi-static single-input multiple-output (SIMO) fading channels. Under mild conditions on the channel gains, it is shown that the channel dispersion is zero regardless of whether the fading realizations are available at the transmitter and/or the receiver. The result follows from computationally and analytically tractable converse and achievability bounds. Through numerical evaluation, we verify that, in some scenarios, zero dispersion indeed entails fast convergence to outage capacity as the blocklength increases. In the example of a particular 1×2 SIMO Rician channel, the blocklength required to achieve 90% of capacity is about an order of magnitude smaller compared to the blocklength required for an AWGN channel with the same capacity.},

keywords = {achievability bounds, AWGN channel, AWGN channels, channel capacity, channel dispersion, channel gains, Dispersion, error probability, error statistics, Fading, fading channels, fading realizations, fast convergence, finite blocklength, maximal achievable rate, numerical evaluation, outage capacity, quasistatic SIMO fading channels, Random variables, Receivers, SIMO Rician channel, single-input multiple-output, Transmitters, zero dispersion},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Bifet2013a,

title = {Efficient Data Stream Classification via Probabilistic Adaptive Windows},

author = {Albert Bifet and Bernhard Pfahringer and Jesse Read and Geoff Holmes},

url = {http://dl.acm.org/citation.cfm?id=2480362.2480516},

isbn = {9781450316569},

year  = {2013},

date = {2013-01-01},

booktitle = {Proceedings of the 28th Annual ACM Symposium on Applied Computing - SAC '13},

publisher = {ACM Press},

address = {Coimbra},

abstract = {In the context of a data stream, a classifier must be able to learn from a theoretically-infinite stream of examples using limited time and memory, while being able to predict at any point. Many methods deal with this problem by basing their model on a window of examples. We introduce a probabilistic adaptive window (PAW) for data-stream learning, which improves this windowing technique with a mechanism to include older examples as well as the most recent ones, thus maintaining information on past concept drifts while being able to adapt quickly to new ones. We exemplify PAW with lazy learning methods in two variations: one to handle concept drift explicitly, and the other to add classifier diversity using an ensemble. Along with the standard measures of accuracy and time and memory use, we compare classifiers against state-of-the-art classifiers from the data-stream literature.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Koblents2013a,

title = {A Population Monte Carlo Scheme for Computational Inference in High Dimensional Spaces},

author = {Eugenia Koblents and Joaquin Miguez},

url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6638881},

issn = {1520-6149},

year  = {2013},

date = {2013-01-01},

booktitle = {2013 IEEE International Conference on Acoustics, Speech and Signal Processing},

pages = {6318--6322},

publisher = {IEEE},

address = {Vancouver},

abstract = {In this paper we address the Monte Carlo approximation of integrals with respect to probability distributions in high-dimensional spaces. In particular, we investigate the population Monte Carlo (PMC) scheme, which is based on an iterative importance sampling (IS) approach. Both IS and PMC suffer from the well known problem of degeneracy of the importance weights (IWs), which is closely related to the curse-of-dimensionality, and limits their applicability in large-scale practical problems. In this paper we investigate a novel PMC scheme that consists in performing nonlinear transformations of the IWs in order to smooth their variations and avoid degeneracy. We apply the modified IS scheme to the well-known mixture-PMC (MPMC) algorithm, which constructs the importance functions as mixtures of kernels. We present numerical results that show how the modified version of MPMC clearly outperforms the original scheme.},

keywords = {Approximation methods, computational inference, degeneracy of importance weights, high dimensional spaces, Importance sampling, importance weights, iterative importance sampling, iterative methods, mixture-PMC, mixture-PMC algorithm, Monte Carlo methods, MPMC, nonlinear transformations, population Monte Carlo, population Monte Carlo scheme, Probability density function, probability distributions, Proposals, Sociology, Standards},

pubstate = {published},

tppubtype = {inproceedings}

}

@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}

}

@inproceedings{Bifet2013b,

title = {Pitfalls in Benchmarking Data Stream Classification and How to Avoid Them},

author = {Albert Bifet and Jesse Read and Indre Zliobaite and Bernhard Pfahringer and Geoff Holmes},

year  = {2013},

date = {2013-01-01},

booktitle = {ECML 2013: 24th European Conference on Machine Learning},

keywords = {COMPREHENSION},

pubstate = {published},

tppubtype = {inproceedings}

}

@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}

}

@inproceedings{Olmos2013c,

title = {A Finite Length Performance Analysis of LDPC Codes Constructed by Connecting Spatially Coupled Chains},

author = {Pablo M Olmos and David G M Mitchell and Dimitri Truhachev and Daniel J Costello},

url = {http://itw2013.tsc.uc3m.es/authors},

year  = {2013},

date = {2013-01-01},

booktitle = {2013 IEEE Information Theory Workshop},

address = {Seville},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@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}

}

@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}

}

@inproceedings{allerton2012,

title = {Converse Bounds for Finite-Length Joint Source-Channel Coding},

author = {Adria Tauste Campo and Gonzalo Vazquez-Vilar and Albert Guill\'{e}n i F\`{a}bregas and Tobias Koch and Alfonso Martinez},

year  = {2012},

date = {2012-10-01},

booktitle = {50th Annual Allerton Conference on Communication, Control and Computing (Allerton 2012)},

address = {Allerton, IL, USA},

note = {Invited},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{iccasp2012,

title = {Design of universal multicoset sampling patterns for compressed sensing of multiband sparse signals},

author = {Mar\'{i}a Elena Dom\'{i}nguez-Jim\'{e}nez and Nuria Gonz\'{a}lez-Prelcic and Gonzalo Vazquez-Vilar and Roberto L\'{o}pez-Valcarce},

year  = {2012},

date = {2012-03-01},

booktitle = {2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2012)},

address = {Kyoto, Japan},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Salamanca2012,

title = {Tree-Structured Expectation Propagation for LDPC Decoding over the AWGN Channel},

author = {Luis Salamanca and Juan Jose Murillo-Fuentes and Pablo M Olmos and Fernando Perez-Cruz},

url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6349716},

issn = {1551-2541},

year  = {2012},

date = {2012-01-01},

booktitle = {2012 IEEE International Workshop on Machine Learning for Signal Processing},

pages = {1--6},

publisher = {IEEE},

address = {Santander},

abstract = {In this paper, we propose the tree-structured expectation propagation (TEP) algorithm for low-density parity-check (LDPC) decoding over the additive white Gaussian noise (AWGN) channel. By imposing a tree-like approximation over the graphical model of the code, this algorithm introduces pairwise marginal constraints over pairs of variables, which provide joint information of the variables related. Thanks to this, the proposed TEP decoder improves the performance of the standard belief propagation (BP) solution. An efficient way of constructing the tree-like structure is also described. The simulation results illustrate the TEP decoder gain in the finite-length regime, compared to the standard BP solution. For code lengths shorter than n = 512, the gain in the waterfall region achieves up to 0.25 dB. We also notice a remarkable reduction of the error floor.},

keywords = {additive white Gaussian noise channel, Approximation algorithms, Approximation methods, approximation theory, AWGN channel, AWGN channels, belief propagation solution, Bit error rate, Decoding, error floor reduction, finite-length regime, Gain, Joints, LDPC decoding, low-density parity-check decoding, pairwise marginal constraint, parity check codes, TEP decoder, tree-like approximation, tree-structured expectation propagation, trees (mathematics)},

pubstate = {published},

tppubtype = {inproceedings}

}