JOURNAL ARTICLES

  • Explicit-duration Markov switching models. S. Chiappa. Foundations and Trends in Machine Learning, 7(6): pages 803-886, 2014 http://www.nowpublishers.com/article/Details/MAL-054. [.pdf]
  • This manuscript gives a unified overview of hidden-semi-Markov/segment models and change-point/reset models.

  • A probabilistic model of biological ageing of the lungs for analysing the effects of smoking, asthma and COPD. S. Chiappa, J. Winn, A. Vinuela, H. Tipney and T. D. Spector. Respiratory Research, 14:60, 2013. DOI: 10.1186/1465-9921-14-60 http://respiratory-research.com/content/14/1/60.

  • Bayesian factorial linear Gaussian state-space models for biosignal decomposition. S. Chiappa and D. Barber. Signal Processing Letters, 14(4): pages 267-270, 2007. [.pdf] (Matlab code)

  • EEG classification using generative independent component analysis. S. Chiappa and D. Barber. Neurocomputing, 69(7-9): pages 769-777, 2006. [.pdf] 

 

 

BOOKS

 

Bayesian time series models, D. Barber, A. T. Cemgil, and S. Chiappa Editors, Cambridge University Press, pages 1-432, August 2011.



BOOK CHAPTERS


  • Inference and learning in latent Markov models. D. Barber and S. Chiappa. To appear in Advanced State Space Methods for Neural and Clinical Data. Z. Chen Editor, Cambridge University Press, 2014.

 

Inference and estimation in probabilistic time series models. D. Barber, A. T. Cemgil, and S. Chiappa. Bayesian Time-Series Models.  D. Barber, A. T. Cemgil, and S. Chiappa Editors, Cambridge University Press, pages 1-31, 2011. [.pdf]

 


CONFERENCE PROCEEDINGS

Movement extraction by detecting dynamics switches and repetitions. S. Chiappa and J. Peters. In Advances in Neural Information Processing Systems 23 (NIPS 24), pages 388-396, 2010. [.pdf].

 

 

A Bayesian approach to graph regression with relevant subgraph selection. S. Chiappa, H. Saigo, and K. Tsuda. In 9th SIAM International Conference on Data Mining (SDM), pages 295-304, 2009. [.pdf]

 

 

Using Bayesian dynamical systems for motion template libraries. S. Chiappa, J. Kober, and J. Peters. In Advances in Neural Information Processing Systems 21 (NIPS 22), pages 297-304, 2009. [.pdf]

(A video with executions of the ball-in-a-cup game of dexterity by an anthropomorphic SARCOS arm is available here) 

 

 

A Bayesian approach to switching linear Gaussian state-space models for unsupervised time-series segmentation.  S. Chiappa. In 7th International Conference on Machine Learning and Applications (ICMLA), pages 3-9, 2008. [.pdf]

 

 

Output grouping using Dirichlet mixtures of linear Gaussian state-space models. S. Chiappa and D. Barber. In 5th IEEE International Symposium on Image and Signal Processing and Analysis (ISPA), pages 446-451, 2007. [.pdf] 

 

 

Unified inference for variational Bayesian linear Gaussian state-space models. D. Barber and S. Chiappa. In Advances in Neural Information Processing Systems 19 (NIPS 20), pages 81-88, 2007. [.pdf]  (corrected version of the proceedings publication: in Algorithm 1 U_AB has been replaced with its transpose and viceversa) (Matlab code)

 

 

Generative independent component analysis for EEG classification. S. Chiappa and D. Barber. In 13th European Symposium on Artificial Neural Networks (ESANN), pages 297-302, 2005.

 

 

Generative temporal ICA for classification in asynchronous BCI systems. S. Chiappa and D. Barber. In 2nd International IEEE EMBS Conference on Neural Engineering, pages 514-517, 2005.

 

 

HMM and IOHMM modeling of EEG rhythms for asynchronous BCI systems. S. Chiappa and S. Bengio. In 12th European Symposium on Artificial Neural Networks (ESANN), pages 199-204, 2004.

 

 

Evolution of the mental states operating a brain-computer interface. J. Mouriño, S. Chiappa, R. Jané, and José del R. Millán. In 24th Annual EMBS International Conference, pages 400-401, 2002.

 

 

Spatial filtering in the training process of a brain computer interface. J. Mouriño, José del R. Millán, F. Cincotti, S. Chiappa, R. Jané, and F. Babiloni. In 23rd Annual EMBS International Conference, pages 639-642, 2001.

 

 

THESIS


Analysis and Classification of EEG Signals using Probabilistic Models for Brain Computer Interfaces. S. Chiappa. Ph.D. thesis 3547, EPF Lausanne, Switzerland, pages 1-131, 2006. 

 

 

Algebre di Banach e Trasformata di Gelfand. S. Chiappa. Tesi di Laurea, Università di Bologna, Italy, pages 1-70, 1999.


 
ABSTRACTS

A variational Bayesian approach to linear Gaussian state-space models. S. Chiappa.
Sixth Workshop on Bayesian Inference in Stochastic Processes, Brixen, Italy, 2009.
 
 
Variational Bayesian model selection in linear Gaussian state-space based models. S. Chiappa.
International Workshop on Flexible Modelling: Smoothing and Robustness, KULeuven, Belgium, 2008.
 
 
Studying Phase Synchrony for Classification of Mental Tasks in Brain Machine Interfaces. E. Gysels, José del R. Millán, S. Chiappa, and P. Celka.  In Conference of the International Society for Brain Electromagnetic Topography, 2003.


THECHNICAL REPORS AND NOTES

  • Unsupervised Bayesian Time-Series Segmentation based on Linear Gaussian State-Space Models. S. Chiappa, Technical Report no. 171. Max-Planck Institute for Biological Cybernetics,Tübingen, Germany, pages 1-11, 2008. 


  • Dirichlet Mixtures of Bayesian Linear Gaussian State-Space Models: a Variational Approach. S. Chiappa and D. Barber. Technical Report no. 161. Max-Planck Institute for Biological Cybernetics,Tübingen, Germany, pages 1-32, 2007. [.pdf] 


  • Inference in the Variational Linear Gaussian State-Space Model using Belief Propagation and the Junction Tree Algorithm. S. Chiappa. Max-Planck Institute for Biological Cybernetics,Tübingen, Germany, pages 1-8, 2007. [.pdf]

Sequence classification with input-output hidden Markov models. S. Chiappa and S. Bengio. IDIAP Research Report 04-13, pages 1-12, 2004.

Nonlinear analysis of cognitive and motor-related EEG signals. S. Chiappa and S. Bengio. IDIAP Research Report 03-14, pages 1-13, 2003.

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