Alex Jung
Assistant Professor, Koneoppiminen ja data-analyysi at Aalto University School of Business
Biography
Aalto University School of Business
I'm interested in any aspect of machine learning for big data applications. Particular focus is given on sparse models (compressed sensing) and on the effects of constraints on computational complexity and communication requirements of the implemented learning algorithms. Recent work considers the fundamental limits of and practical methods for dictionary learning and graphical model selection for high-dimensional processes.
Peer-reviewed scientific articles
Journal article-refereed, Original researchOn the minimax risk of dictionary learning
Jung, Alexander; Eldar, Yonina C.; Görtz, Norbert2016 in IEEE Transactions on information theory (IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC)ISSN: 0018-9448Learning the Conditional Independence Structure of Stationary Time Series: A Multitask Learning Approach
Jung, Alexander2015 in IEEE TRANSACTIONS ON SIGNAL PROCESSING (IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC)ISSN: 1053-587XConference proceedingsSmooth graph signal recovery via efficient Laplacian solvers
Eslamlou, Gita Babazadeh; Jung, Alexander; Goertz, Norbert2017 ISBN: 9781509041176Efficient Graph Signal Recovery over Big Networks
Hannak, Gabor; Berger, Peter; Matz, Gerald; Jung, Alex2017 in Conference Record of the Asilomar Conference on Signals Systems and Computers (IEEE COMPUTER SOCIETY PRESS)ISBN: 9781538639542ISSN: 1058-6393Learning conditional independence structure for high-dimensional uncorrelated vector processes
Quang, Nguyen Tran; Jung, Alexander2017 ISBN: 9781509041176Graph signal recovery from incomplete and noisy information using approximate message passing
Eslamlou, Gita Babazadeh; Jung, Alex; Goertz, Norbert; Fereydooni, Mehdi2016 ISBN: 9781479999880Scalable graph signal recovery for big data over networks
Jung, Alex; Berger, Peter; Hannak, Gabor; Matz, Gerald2016 ISBN: 9781509017492
On the minimax risk of dictionary learning
Learning the Conditional Independence Structure of Stationary Time Series: A Multitask Learning Approach
Smooth graph signal recovery via efficient Laplacian solvers
Efficient Graph Signal Recovery over Big Networks
Learning conditional independence structure for high-dimensional uncorrelated vector processes
Graph signal recovery from incomplete and noisy information using approximate message passing
Scalable graph signal recovery for big data over networks
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