New paper accepted for publication in Signal Processing

The paper “Improving Population Monte Carlo: Alternative Weighting and Resampling Schemes” by  V. Elvira, L. Martino, D. Luengo, and M. Bugallo has been accepted for publication in Signal Processing Abstract: Population Monte Carlo (PMC) sampling methods are powerful tools for approximating distributions of static unknowns given a set of observations. These methods are iterative in nature: at…

New paper accepted for publication in the IEEE Trans. Inf. Theory

The paper “Multi-Class Source-Channel Coding” by  I. E. Bocharova, A. Guillen i Fabregas, B. D. Kudryashov, A. Martinez, A. Tauste Campo and G. Vazquez-Vilar has been accepted for publication in the IEEE Transactions on Information Theory. Abstract: This paper studies an almost-lossless source-channel coding scheme in which source messages are assigned to different classes and encoded with…

Research Work GTS at 2016 IEEE International Symposium on Information Theory

The following group members will be presenting their work at the upcoming 2016 IEEE International Symposium on Information Theory Tobias Koch and Gonzalo Vazquez-Vilar, «A General Rate-Distortion Converse Bound for Entropy-Constrained Scalar Quantization” Markus Stinner (TUM), Luca Barletta (Politecnico di Milano) and Pablo M. Olmos, «Finite-Length Scaling Based on Belief Propagation for Spatially Coupled LDPC…

New paper accepted

A new paper from the group has been accepted for publication The paper “The Shannon lower bound is asymptotically tight” by T. Koch has been accepted for publication in the IEEE Transactions on Information Theory. Abstract: The Shannon lower bound is one of the few lower bounds on the rate-distortion function that holds for a…

New paper accepted

A new paper from the group has been accepted for publication The paper “Bayesian M-ary Hypothesis Testing: The Meta-Converse and Verdú-Han Bounds are Tight” by G. Vazquez-Vilar, A. Tauste Campo, A. Guillén i Fàbregas and A. Martinez has been accepted for publication in IEEE Transactions on Information Theory. Abstract: Two alternative exact characterizations of the minimum error probability…

New paper accepted

A new paper from the group has been accepted for publication The paper “Layered Adaptive Importance Sampling” by L. Martino, V. Elvira, D. Luengo, and J. Corander has been accepted for publication in Statistics and Computing. Abstract: Monte Carlo methods represent the «de facto» standard for approximating complicated integrals involving multidimensional target distributions. In order to generate random realizations from…

New paper accepted

A new paper from the group has been accepted for publication The paper «Short-Packet Communications over Multiple-Antenna Rayleigh-Fading Channels» by Giuseppe Durisi, Tobias Koch, Johan Östman, Yury Polyanskiy, and Wei Yang has been accepted for publication in the IEEE Transactions on Communications. Abtract: Motivated by the current interest in ultra-reliable, low-latency, machine-type communication systems, we…

New papers accepted

Two new papers from the group have been accepted for their publication: The paper «On the Waterfall Performance of Finite-length SC-LDPC Codes Constructed from Protographs» by Markus Stinner and Pablo M. Olmos has been accepted for publication in IEEE Journal on Selected Areas in Communications, special issue on Recent Advances in Capacity Approaching codes. November…

New paper accepted

A new paper from the group has been accepted for publication The paper «Human Activity Recognition by Combining a Small Number of Classifiers » by Alfredo Nazábal,  Pablo G. Moreno,  Antonio Artés-Rodríguez and Zoubin Ghahramani has been accepted for publication in IEEE Journal of Biomedical and Health Informatics. Abtract: We consider the problem of daily…

New paper accepted

A new paper from the group has been accepted for publication The paper «Bayesian Nonparametric Crowdsourcing» by Pablo G. Moreno, Yee Whye Teh, Fernando Perez-Cruz and Antonio Artés-Rodríguez has been accepted for publication in Journal of Machine Learning Research. Abtract: Crowdsourcing has been proven to be an effective and efficient tool to annotate large datasets.…