A. Jung

Alexander Jung, Gerald Matz, Peter Berger, 2016, 2016 IEEE 17th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC).

Graphical LASSO based Model Selection for Time Series

Alexander Jung, Norbert Goertz, Gabor Hannak, 2014, IEEE Signal Processing Letters.

Learning the Conditional Independence Structure of Stationary Time Series: A Multitask Learning Approach

Alexander Jung, A. Jung, 2014, IEEE Transactions on Signal Processing.

Compressive nonparametric graphical model selection for time series

Helmut Bölcskei, Alexander Jung, Reinhard Heckel, 2013, 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

Dynamic Sparse Subspace Clustering for Evolving High-Dimensional Data Streams

Li Liu, Xiang Li, Jinping Sui, 2020, IEEE Transactions on Cybernetics.

Clustering in Partially Labeled Stochastic Block Models via Total Variation Minimization pdf

Alexander Jung, A. Jung, 2019, 2020 54th Asilomar Conference on Signals, Systems, and Computers.

On the Duality Between Network Flows and Network Lasso

Alexander Jung, A. Jung, 2019, IEEE Signal Processing Letters.

Semi-Supervised Learning in Network-Structured Data via Total Variation Minimization

Yonina C. Eldar, Alexander Jung, Alfred O. Hero, III, 2019, IEEE Transactions on Signal Processing.

Learning Networked Exponential Families with Network Lasso pdf

Alexander Jung, A. Jung, 2019, ArXiv.

On the Complexity of Sparse Label Propagation pdf

Alexander Jung, A. Jung, 2018, Front. Appl. Math. Stat..

The Network Nullspace Property for Compressed Sensing of Big Data Over Networks

Alexander Jung, A. Jung, 2017, Front. Appl. Math. Stat..

Scalable Semi-Supervised Learning over Networks using Nonsmooth Convex Optimization pdf

Alfred O. Hero, Alexander Jung, Sabeur Aridhi, 2016, ArXiv.

Towards Model-Agnostic Federated Learning over Networks

A. Jung, 2023, 2302.04363.

Classifying Big Data Over Networks Via The Logistic Network Lasso

Alexander Jung, Nguyen Tran, Henrik Ambos, 2018, 2018 52nd Asilomar Conference on Signals, Systems, and Computers.

The RKHS Approach to Minimum Variance Estimation Revisited: Variance Bounds, Sufficient Statistics, and Exponential Families

Alexander Jung, Franz Hlawatsch, Sebastian Schmutzhard, 2012, IEEE Transactions on Information Theory.

Minimum variance estimation for the sparse signal in noise model

Alexander Jung, Franz Hlawatsch, Sebastian Schmutzhard, 2011, 2011 IEEE International Symposium on Information Theory Proceedings.

Unbiased Estimation of a Sparse Vector in White Gaussian Noise

Yonina C. Eldar, Alexander Jung, Franz Hlawatsch, 2010, IEEE Transactions on Information Theory.

An Information-Theoretic Approach to Personalized Explainable Machine Learning

Alexander Jung, Pedro H. J. Nardelli, A. Jung, 2020, IEEE Signal Processing Letters.

Random walk sampling for big data over networks

Alexander Jung, Saeed Basirian, A. Jung, 2017, 2017 International Conference on Sampling Theory and Applications (SampTA).

Learning Explainable Decision Rules via Maximum Satisfiability

Alexander Jung, Henrik E. C. Cao, Riku Sarlin, 2020, IEEE Access.

Sparse Subspace Clustering for Evolving Data Streams

Zhen Liu, Alexander Jung, Xiang Li, 2019, ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

Compressive Spectral Estimation for Nonstationary Random Processes

Alexander Jung, Franz Hlawatsch, Georg Tauböck, 2012, IEEE Transactions on Information Theory.

Iterative Recovery of Dense Signals from Incomplete Measurements

Alexander Jung, Mike E. Davies, Norbert Goertz, 2014, IEEE Signal Processing Letters.

Big Data Frameworks: A Comparative Study pdf

Alexander Jung, Sabeur Aridhi, Haithem Mezni, 2016, ArXiv.

Towards Model-Agnostic Federated Learning over Networks

A. Jung, 2023, ArXiv.

On the Sample Complexity of Graphical Model Selection for Non-Stationary Processes pdf

Alexander Jung, Nguyen Tran Quang, A. Jung, 2017, ArXiv.

Nonparametric Compressive Graphical Model Selection for Vector-Valued Stationary Random Processes: A Multitask Learning Approach

Alexander Jung, A. Jung, 2014 .

Classifying Partially Labeled Networked Data VIA Logistic Network Lasso

Alexander Jung, Nguyen Tran, Henrik Ambos, 2019, ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

Performance limits of dictionary learning for sparse coding

Yonina C. Eldar, Alexander Jung, Norbert Goertz, 2014, 2014 22nd European Signal Processing Conference (EUSIPCO).

Ju n 20 14 PERFORMANCE LIMITS OF DICTIONARY LEARNING FOR SPARSE CODIN G

Yonina C. Eldar, A. Jung, Norbert G̈ortz, 2021 .

Local Graph Clustering With Network Lasso

Alexander Jung, Yasmin SarcheshmehPour, C. Aggarwal, 2020, IEEE Signal Processing Letters.

Moreau Envelope ADMM for Decentralized Weakly Convex Optimization

Naveen K. D. Venkategowda, A. Jung, Reza Mirzaeifard, 2023, ArXiv.

Learning to Avoid Errors in GANs by Manipulating Input Spaces pdf

Alexander B. Jung, A. Jung, 2017, ArXiv.

An Information-Theoretic Approach to Explainable Machine Learning pdf

Alexander Jung, A. Jung, 2020, ArXiv.

Basic Principles of Clustering Methods pdf

Alexander Jung, Ivan Baranov, A. Jung, 2019, ArXiv.

Analysis of Network Lasso For Semi-Supervised Regression

A. Jung, 2018, ArXiv.

Recovery conditions and sampling strategies for network Lasso

Alexander Jung, Alexandru Mara, A. Jung, 2017, 2017 51st Asilomar Conference on Signals, Systems, and Computers.

Graph Signal Sampling via Reinforcement Learning

Alexander Jung, Oleksii Abramenko, A. Jung, 2018, ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

The network nullspace property for compressed sensing over networks

Yonina C. Eldar, Alexander Jung, Ayelet Heimowitz, 2017, 2017 International Conference on Sampling Theory and Applications (SampTA).

Sparse Label Propagation pdf

Alexander Jung, A. Jung, 2016, ArXiv.

On the information-theoretic limits of graphical model selection for Gaussian time series

Alexander Jung, Norbert Goertz, Gabor Hannak, 2014, 2014 22nd European Signal Processing Conference (EUSIPCO).

Clustered Federated Learning via Generalized Total Variation Minimization

A. Jung, Yu Tian, Linli Zhang, 2021, IEEE Transactions on Signal Processing.

Performance bounds for sparse parametric covariance estimation in Gaussian models

Alfred O. Hero, Alexander Jung, Franz Hlawatsch, 2011, 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

When Is Network Lasso Accurate?

Nguyen Tran Quang, A. Jung, A. Mara, 2017, Front. Appl. Math. Stat..