Publication List, April 2021

Books

  • A. Jung, ‘‘Machine Learning: The Basics’’, to be published by Springer, 2021. current draft.

Papers at international journals (peer-reviewed)

  1. R. Tervo, I. Láng, A. Jung, and A. Mäkelä, “Predicting power outages caused by extratropical storms,” in Natural Hazards and Earth System Sciences, 21(2), pp. 607-627, 2021. weblink.

  2. A. Jung and Y. SarcheshmehPour, “Local Graph Clustering With Network Lasso,” in IEEE Signal Processing Letters, vol. 28, pp. 106-110, 2021, doi: 10.1109/LSP.2020.3045832.

  3. H. Cao, R. Sarlin, and A. Jung, ‘‘Learning Explainable Decision Rules via Maximum Satisfiability,’’ In IEEE Access, Vol. 8. pp. 218180-218185, 2020. doi: 10.1109/ACCESS.2020.3041040.

  4. A. Jung, ‘‘Networked Exponential Families For Big Data over Networks,’’ in IEEE Access, Oct. 2020. doi: 10.1109/ACCESS.2020.3033817.

  5. J. Sui, Z. Liu, L. Liu, A. Jung, and X. Li, "Dynamic Sparse Subspace Clustering for Evolving High-dimensional Data Streams,’’ in IEEE Trans. Cyb., 2020. doi: 10.1109/TCYB.2020.3023973.

  6. A. Jung, ‘‘On the Duality Between Network Flows and Network Lasso,’’ in IEEE Signal Processing Letters, vol. 27, pp. 940-944, 2020, doi: 10.1109/LSP.2020.2998400.

  7. A. Jung and P. H. J. Nardelli, ‘‘An Information-Theoretic Approach to Personalized Explainable Machine Learning,’’ in IEEE Signal Processing Letters, vol. 27, pp. 825-829, 2020, doi: 10.1109/LSP.2020.2993176.

  8. N. Tran, O. Abramenko, A. Jung, ‘‘On the Sample Complexity of Graphical Model Selection from Non-Stationary Samples,’’ in IEEE Trans. Sig. Proc., vol. 68, pp. 17-32, 2020. preprint.

  9. A. Jung, A.O. Hero III, A. Mara, S. Jahromi, A. Heimowitz, Y. C. Eldar, ‘‘Semi-Supervised Learning in Network-Structured Data via Total Variation Minimization,’’ in IEEE Trans. Sig. Proc., vol. 67 , nr. 24, Dec. 2019, preprint.

  10. R. Tervo, J. Karjalainen and A. Jung, ‘‘Short-Term Prediction of Electricity Outages Caused by Convective Storms,’’ in IEEE Trans. on Geosc. and Rem. Sens., June 2019. DOI 10.1109/TGRS.2019.2921809

  11. A. Jung, N. Tran, ‘‘Localized Linear Regression in Networked Data,’’ in IEEE Sig. Proc. Letters, July 2019. DOI: 10.1109/LSP.2019.2918933. preprint.

  12. J. Sui, Z. Liu, A. Jung, L. Liu, X. Li, ‘‘Dynamic Clustering Scheme for Evolving Data Streams Based on Improved STRAP,’’ in IEEE Access, Aug. 2018. DOI: 10.1109/ACCESS.2018.2864553.

  13. A. Jung, ‘‘On the Complexity of Sparse Label Propagation,’’ in Front. Appl. Math. Stat., Jul. 2018. DOI: 10.3389/fams.2018.00022.

  14. A. Jung, N. Tran, A. Mara, ‘‘When Is Network Lasso Accurate?,’’ in Front. Appl. Math. Stat., Mar. 2018. DOI: 10.3389/fams.2017.00028.

  15. A. Jung, ‘‘A Fixed-Point of View on Gradient Methods for Big Data,’’ in Front. Appl. Math. Stat., Sept. 2017. DOI: 10.3389/fams.2017.00018.

  16. A. Jung, Y. C. Eldar, N. Görtz, ‘‘On the Minimax Risk of Dictionary Learning,’’ in IEEE Trans. Inf. Theory, vol. 62, no. 3, p. 1501 - 1515, Mar. 2016. DOI: 10.1109/TIT.2016.2517006.

  17. A. Jung, ‘‘Learning the Conditional Independence Structure of Stationary Time Series: A Multitask Learning Approach,’’ in IEEE Trans. Sig. Proc., vol. 63, no. 21, p. 5677 - 5690, Nov. 2015. DOI: 10.1109/TSP.2015.2460219.

  18. A. Jung, G. Hannak, N. Görtz, ‘‘Graphical LASSO Based Model Selection for Time Series,’’ in IEEE Sig. Proc. Letters, vol. 22, no. 10, p. 1781 - 1785, Oct. 2015. DOI: 10.1109/LSP.2015.2425434.

  19. A. Jung, S. Schmutzhard, F. Hlawatsch, Z. Ben-Haim, Y. C. Eldar, ‘‘Minimum Variance Estimation of Sparse Vectors within the Linear Gaussian Model: An RKHS Approach, ’’ in IEEE Trans. Inf. Theory, vol. 60, no. 10, Oct. 2014. DOI: 10.1109/TIT.2014.2346508.

  20. N. Görtz, C. Guo, A. Jung, M. E. Davies, G. Doblinger, ‘‘Iterative Recovery of Dense Signals from Incomplete Measurements, ’’ in IEEE Sig. Proc. Letters, vol. 21, no. 9, p. 1059 - 1063, Sept. 2014. DOI: 10.1109/LSP.2014.2323973.

  21. A. Jung, S. Schmutzhard, F. Hlawatsch, ‘‘The RKHS Approach to Minimum Variance Estimation Revisited: Variance Bounds, Sufficient Statistics, and Exponential Families, ’’ in IEEE Trans. Inf. Theory, vol. 60, no. 7, p. 4050 - 4065, July 2014. DOI: 10.1109/TIT.2014.2317176.

  22. A. Jung, G. Tauböck, F. Hlawatsch, ‘‘Compressive Spectral Estimation for Nonstationary Random Processes,’’ in IEEE Trans. Inf. Theory, vol. 59, no. 5, p. 3117 - 3138, May 2013. DOI: 10.1109/TIT.2012.2237475.

  23. A. Jung, Z. Ben-Haim, F. Hlawatsch, Y. C. Eldar, ‘‘Unbiased Estimation of a Sparse Vector in White Gaussian Noise,’’ in IEEE Trans. Inf. Theory, vol. 57, no. 12, p. 7856 - 7876, Dec. 2011. DOI: 10.1109/TIT.2011.2170124

Papers at international conferences (peer-reviewed)

  1. Y. Sarcheshmehpour, M. Leinonen, A. Jung, ‘‘Federated Learning from Big Data over Networks,’’ to be presented at IEEE Int. Conf. on Acoustics, Speech and Sig. Proc., 2021.

  2. A. Jung, ‘‘Clustering in Partially Labeled Stochastic Block Models via Total Variation Minimization,’’ in Proc.emph{Asilomar Conf. Signals, Systems, Computers}, Nov. 2020. preprint.

  3. T. Huuhtanen and A. Jung, ‘‘Anomaly Location Detection with Electrical Impedance Tomography Using Multilayer Perceptrons,’’ in Proc. IEEE Int. Workshop on Machine Learning for Sig. Proc., Espoo, Finland, Sept., 2020.

  4. T. Huuhtanen, A. Lankinen, and A. Jung, ‘‘Target Tracking on Sensing Surface with Electrical Impedance Tomography,’’ in Proc. European Signal Processing Conference, EUSIPCO, pp. 1817-1821, Aug., 2020.

  5. N. Tran, H. Ambos and A. Jung, ‘‘Classifying Partially Labeled Networked Data via Logistic Network Lasso,’’ in Proc. IEEE International Conference on Acoustics, Speech and Signal Processing, Barcelona, Spain, 2020, pp. 3832-3836, doi: 10.1109/ICASSP40776.2020.9054408.

  6. O. Abramenko, A. Jung, ‘‘Graph Signal Sampling via Reinforcement Learning,’’ in Proc. IEEE International Conference on Acoustics, Speech, and Signal Processing, May 2019.

  7. J. Sui, Z. Liu, L. Liu, A. Jung, T. Liu, B. Peng, X. Li, ‘‘Sparse Subspace Clustering for Evolving Data Streams,’’ in Proc. IEEE Int. Conf. on Acoustics, Speech, and Signal Processing, May 2019.

  8. A. Jung, N. Vesselinova, ‘‘Analysis of Network Lasso for Semi-Supervised Regression,’’ in Proc. Int. Conf. on Artificial Intelligence and Statistics (AISTATS), Apr. 2019.

  9. J. Kahles, J. T{"o}rr{"o}nen, T. Huuhtanen, A. Jung, ‘‘Automating Root Cause Analysis via Machine Learning in Agile Software Testing Environments,’’ in Proc. IEEE Conference on Software Testing, Validation and Verification, Apr. 2019.

  10. H. Ambos, N. Tran, A. Jung, ‘‘Classifying Big Data over Networks via the Logistic Network Lasso,’’ in Proc. Asilomar Conf. Signals, Systems, Computers, Nov. 2018.

  11. M. Hinkka, T. Lehto, K. Heljanko, A. Jung, ‘‘Classifying Process Instances Using Recurrent Neural Networks,’’ in Proc. Business Process Management Workshops, Sept. 2018.

  12. N. Tran, H. Ambos, A. Jung, ‘‘A Network Compatibility Condition for Compressed Sensing over Complex Networks,’’ in Proc. IEEE Statistical Signal Processing Workshop, Jun. 2018. DOI: 10.1109/SSP.2018.8450811.

  13. B. Atli, Y. Miche, A. Jung, ‘‘Network Intrusion Detection Using Flow Statistics,’’ in Proc. IEEE Statistical Signal Processing Workshop, Jun. 2018. DOI: 10.1109/SSP.2018.8450709.

  14. T. Huuhtanen and A. Jung, ‘‘Predictive Maintenance of Photovoltaic Panels via Deep Learning,’’ in Proc. IEEE Data Science Workshop, Jun. 2018. DOI: 10.1109/DSW.2018.8439898.

  15. R. Tervo, J. Karjalainen, A. Jung, ‘‘Predicting Electricity Outages Caused by Convective Storms,’’ in Proc. IEEE Data Science Workshop, Jun. 2018. DOI: 10.1109/DSW.2018.8439906.

  16. M. Hulsebos, A. Jung, ‘‘The network nullspace property for compressed sensing of big data over networks,’’ in Proc. IEEE International Conference on Acoustics, Speech, and Signal Processing, Apr. 2018. DOI: 10.1109/ICASSP.2018.8462504.

  17. N. Tran, A. Jung, ‘‘On the sample complexity of graphical model selection from non-stationary samples,’’ in Proc. IEEE International Conference on Acoustics, Speech, and Signal Processing, Apr. 2018. DOI: 10.1109/ICASSP.2018.8462689.

  18. A. Mara, A. Jung, ‘‘Recovery Conditions and Sampling Strategies for Network Lasso,’’ in Proc. Asilomar Conf. Signals, Systems, Computers, Nov. 2017 (best student paper finalist).

  19. M. Hinkka, T. Lehto, K. Heljanko, A. Jung, ‘‘Structural Feature Selection for Event Logs,’’ in Proc. Workshop on BP Innovations with Artificial Intelligence (BPAI), Sept. 2017.

  20. L. Sayfullina, E. Malmi, Y. Liao, A. Jung, ‘‘Domain Adaptation for Resume Classification Using Convolutional Neural Networks,’’ in Proc. 6th Int. Conf. on Analysis of Images, Social networks and Texts, July 2017.

  21. A. Jung, A. Heimowitz, Y. C. Eldar, ‘‘The Network Nullspace Property for Compressed Sensing over Networks,’’ in Proc. SampTA 2017, Tallinn, Estonia, July 2017. DOI: 10.1109/SAMPTA.2017.8024392.

  22. S. Basirian, A. Jung, ‘‘Random Walk Sampling for Big Data over Networks,’’ in Proc. SampTA 2017, Tallinn, Estonia, July 2017. DOI: 10.1109/SAMPTA.2017.8024453.

  23. N. Tran Quang, A. Jung, ‘‘Learning Conditional Independence Structure for High-Dimensional Uncorrelated Vector Processes,’’ in Proc. IEEE ICASSP 2017, New Orleans, LA, Mar. 2017. DOI: 10.1109/ICASSP.2017.7953292.

  24. G. Babazadeh-Eslamlou, A. Jung, N. Goertz, ‘‘Smooth Graph Signal Recovery via Efficient Laplacian Solvers,’’ in Proc. IEEE ICASSP 2017, New Orleans, LA, Mar. 2017. DOI: 10.1109/ICASSP.2017.7953291.

  25. G. Hannak, P. Berger, G. Matz, A. Jung, ‘‘Efficient Graph Signal Recovery over Big Networks,’’ in Proc. 50th Asilomar Conf. Signals, Systems, Computers 2016. DOI: 10.1109/ACSSC.2016.7869702.

  26. A. Jung, P. Berger, G. Hannak, G. Matz,‘‘Scalable Graph Signal Recovery for Big Data over Networks,’’ in Proc. IEEE Int. Workshop Sig. Proc. Adv. in Wireless Comm., SPAWC 2016, p. 1 - 6, July 2016. DOI: 10.1109/SPAWC.2016.7536869.

  27. G. Babazadeh-Eslamlou, A. Jung, N. Görtz, M. Fereydooni, ‘‘Graph Signal Recovery from Incomplete and noisy Information using Approximate Message Passing,’’ in Proc. IEEE ICASSP 2016, Shanghai, CN, Mar. 2016. DOI: 10.1109/ICASSP.2016.7472863.

  28. G. Hannak, M. Mayer, A. Jung, G. Matz and N. Görtz, ‘‘Joint channel estimation and activity detection for multiuser communication systems,’’ in Proc. IEEE ICC 2015 - Workshop on Massive Uncoordinated Access Protocols, London, UK, pp. 2086 -2091, June 2015. DOI: 10.1109/ICCW.2015.7247489.

  29. G. Hannak, A. Jung, N. Görtz, ‘‘On the Information-Theoretic Limits of Graphical Model Selection for Gaussian Time Series,’’ in Proc. EUSIPCO 2014}, Lisbon, Portugal, Sept. 2014. ISBN: 978-0-9928-6261-9.

  30. A. Jung, Y. C. Eldar, N. Görtz, ‘‘Performance Limits of Dictionary Learning for Sparse Coding,’’ in Proc. EUSIPCO 2014}, Lisbon, Portugal, Sept. 2014. ISBN: 978-0-9928-6261-9.

  31. A. Jung, R. Heckel, F. Hlawatsch, H. Bölcskei, ‘‘Compressive Nonparametric Graphical Model Selection for Time Series,’’ in Proc. IEEE ICASSP 2014, Firenze, Italy pp. 769 - 773, May 2014. DOI: 10.1109/ICASSP.2014.6853700.

  32. S. Schmutzhard, A. Jung, F. Hlawatsch, ‘‘Minimum variance estimation for the sparse signal in noise model,’’ in emph{Proc. IEEE ISIT 2011}, Saint Petersburg, Russia, pp. 124 - 128, JulyAug. 2011. DOI: 10.1109ISIT.2011.6033735.

  33. A. Jung, S. Schmutzhard, F. Hlawatsch, A. O. Hero III, ‘‘Performance bounds for sparse parametric covariance estimation in Gaussian models,’’ in Proc. IEEE ICASSP 2011, Prague, Czech Republic, pp. 4156 - 4159, May 2011. DOI: 10.1109/ICASSP.2011.5947268. (best student paper award)

  34. S. Schmutzhard, A. Jung, F. Hlawatsch, Z. Ben-Haim, Y. C. Eldar, ‘‘A lower bound on the estimator variance for the sparse linear model,’’ in Proc. 44th Asilomar Conf. Signals, Systems, Computers 2010, pp. 1976 - 1980. DOI: 10.1109/ACSSC.2010.5757886.

  35. A. Jung, Z. Ben-Haim, F. Hlawatsch, Y. C. Eldar, ‘‘On unbiased estimation of sparse vectors corrupted by Gaussian noise,’’ in Proc. IEEE ICASSP 2010, Dallas, TX, pp. 3990 - 3993, Mar. 2010. DOI: 10.1109/ICASSP.2010.5495781.

  36. A. Jung, G. Tauböck, F. Hlawatsch, ‘‘Compressive nonstationary spectral estimation using parsimonious random sampling of the ambiguity function,’’ in Proc. IEEE SSP-09, Cardiff, Wales, UK, pp. 642 - 645, Aug.Sept. 2009. DOI: 10.1109SSP.2009.5278493.

  37. A. Jung, G. Tauböck, F. Hlawatsch, ‘‘Compressive spectral estimation for nonstationary random processes,’’ in Proc. IEEE ICASSP 2009, Taipei, Taiwan, R.O.C. pp. 3029 - 3032, April 2009. DOI: 10.1109/ICASSP.2009.4960262.

Papers at international conferences (peer-reviewed, without proceedings entry)

  • N. Tran, S. Basirian, A. Jung., ‘‘When is Network Lasso Accurate: The Vector Case,’’ poster and lecture at NeurIPS Workshop on Learning on Distributions, Functions, Graphs and Groups, Dec. 2017.

Preprints

  • A. Jung, ‘‘Explainable Empirical Risk Minimization,’’ arXiv e-prints, 2020.

Invited articles and presentations

  • A. Jung, ‘‘Information-Theoretic Limits of Learning Networks for Big Data,’’ The Tenth Workshop on Information Theoretic Methods in Science and Engineering (WITMSE), Sept. 2017.