Publications

2016

  1. G. Babazadeh Eslamlou, A. Jung, N. Goertz, M. Fereydooni. Graph signal recovery from incomplete and noisy information using approximate message passing. In Proceedings of the 41st International Conference on Acoustics, Speech, and Signal Processing (ICASSP '16), IEEE, 6170-6174, 2016. [pdf]
  2. S. Aridhi, M. Brugnara, A. Montresor, and Y. Velegrakis. Distributed k-core decomposition and maintenance in large dynamic graphs. In Proceedings of the 10th ACM International Conference on Distributed and Event-based Systems (DEBS '16). Irvine, CA, USA, 161-168, 2016. [pdf]
  3. S. Aridhi, A. Montresor, and Y. Velegrakis. BLADYG: A Novel Block-Centric Framework for the Analysis of Large Dynamic Graphs. In Proceedings of the ACM Workshop on High Performance Graph Processing (HPGP '16). Kyoto, Japan, 39-42, 2016. [pdf]
  4. A. Jung, Y. C. Eldar and N. Gortz. On the Minimax Risk of Dictionary Learning. IEEE Transactions on Information Theory, vol. 62, no. 3, pp. 1501-1515, 2016. [pdf]
  5. S. Aridhi, H. Sghaier, M. Zoghlami, M. Maddouri and E. Mephu Nguifo. Prediction of ionizing radiation resistance in bacteria using a multiple instance learning model. Journal of Computational Biology (JCB), 23(1): 10-20, 2016. [pdf]

    6. 2015

  6. A. Jung. Learning the Conditional Independence Structure of Stationary Time Series: A Multitask Learning Approach. In IEEE Transactions on Signal Processing, vol.63, no.21, pp.5677-5690, Nov.1, 2015. [pdf]
  7. A. Jung, G. Hannak, N. Goertz. Graphical LASSO based Model Selection for Time Series. IEEE Signal Process. Lett., 22(10): 1781-1785, 2015. [pdf]
  8. G. Hannak, M. Mayer, A. Jung, G. Matz, N. Goertz. Joint channel estimation and activity detection for multiuser communication systems. In Communication Workshop (ICCW), pp.2086-2091, 8-12 June 2015.
  9. S. Aridhi, P. Lacomme, L. Ren, B. Vincent. A MapReduce-based approach for shortest path problem in large-scale networks. Engineering Applications of Artificial Intelligence, Elsevier, 41, pp. 151-165, 2015, ISSN 0952-1976. [pdf]
  10. S. Aridhi, L. d'Orazio, M. Maddouri and E. Mephu Nguifo. Density-based data partitioning strategy to approximate large-scale subgraph mining. Information Systems, Elsevier, 48, pp. 213-223, 2015, ISSN 0306-4379. [pdf]
  11. S. Aridhi, L. d'Orazio, M. Maddouri and E. Mephu Nguifo. Cost Models for Distributed Pattern Mining in the Cloud. in IEEE International Conference on Big Data Science and Engineering, vol.2, pp.112-119, 20-22 Aug. 2015, Helsinki, Finland. [pdf]

    12. 2014

  12. N. Goertz, G. Chunli, A. Jung, M.E. Davies, G. Doblinger. Iterative Recovery of Dense Signals from Incomplete Measurements. in IEEE Signal Processing Letters, vol.21, no.9, pp.1059-1063, Sept. 2014.
  13. A. Jung, S. Schmutzhard, F. Hlawatsch. The RKHS Approach to Minimum Variance Estimation Revisited: Variance Bounds, Sufficient Statistics, and Exponential Families, in IEEE Transactions on Information Theory, vol.60, no.7, pp.4050-4065, July 2014.
  14. A. Jung, S. Schmutzhard, F. Hlawatsch, Z. Ben-Haim, Y.C. Eldar. Minimum Variance Estimation of a Sparse Vector Within the Linear Gaussian Model: An RKHS Approach, in IEEE Transactions on Information Theory, vol.60, no.10, pp.6555-6575, Oct. 2014.
  15. G. Hannak, A. Jung, N. Goertz. On the information-theoretic limits of graphical model selection for Gaussian time series, in Proceedings of the 22nd European Signal Processing Conference (EUSIPCO), vol., no., pp.516-520, 1-5 Sept. 2014. [pdf]
  16. A. Jung, Y.C. Eldar, N. Gortz. Performance limits of dictionary learning for sparse coding, in Proceedings of the 22nd European Signal Processing Conference (EUSIPCO), vol., no., pp.765-769, 1-5 Sept. 2014.
  17. A. Jung, R. Heckel, H. Bolcskei, F. Hlawatsch. Compressive nonparametric graphical model selection for time series, in IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), vol., no., pp.769-773, 4-9 May 2014. [pdf]

    18. 2013

  18. A. Jung, G. Taubock, F. Hlawatsch. Compressive Spectral Estimation for Nonstationary Random Processes. In IEEE Transactions on Information Theory, 59(5): 3117-3138, 2013.
  19. S. Aridhi, H. Sghaier, M. Maddouri and E. Mephu Nguifo. Computational phenotype prediction of ionizing-radiation-resistant bacteria with a multiple-instance learning model. In Proceedings of the 12th International Workshop on Data Mining in Bioinformatics (BioKDD '13). ACM, New York, NY, USA, 18-24. [pdf]