Contact

Head of lab

Prof. Dr. Jörg Lücke

+49 441 798 5486

+49 441 798-3902

W30 2-201

Lab Administration

Nicole Kulbach

+49 441 798-3326

+49 441 798-3903

W30 2-206

Postal Address

Prof. Dr. Jörg Lücke
Arbeitsgruppe Machine Learning
Exzellenzcluster Hearing4all und
Department für Medizinische Physik und Akustik
Fakultät für Medizin und Gesundheitswissenschaften
Carl von Ossietzky Universität Oldenburg
D-26111 Oldenburg

Office Address

Room 201 (2nd floor)
Building W30 (NeSSy)  
Küpkersweg 74
26129 Oldenburg

News

2 Feb 2024
Our paper Velychko et al. (preprint) has been accepted at AISTATS 2024.

23 Aug 2023
Our paper "Generic Unsupervised Optimization for a Latent Variable Model with Exponential Family Observables" (Mousavi et al.) has been accepted for publication by the Journal of Machine Learning Research.

22 Feb 2023
Joanna Luberadzka successfully defended her doctoral thesis. Congratulations!

20 Jan 2023
Our paper “The ELBO of Variational Autoencoders Converges to a Sum of Entropies” has been accepted for presentation at the AISTATS 2023 conference.

1 Dec 2022
The paper on sublinear variational clustering by Florian Hirschberger, Dennis Forster and Jörg Lücke has now been published by TPAMI.

4 Nov 2022
Jakob Drefs successfully defended his doctoral thesis "Evolutionary Variational Optimization for Probabilistic Unsupervised Learning". Congratulations!

21 Sept 2022
Jakob Drefs has presented his work at the ECML main conference.

14 June 2022
Our paper "Direct Evolutionary Optimization of Variational Autoencoders With Binary Latents" (Drefs et al.) has been accepted at ECML 2022.

23 March 2022
Yidi Ke has joined our lab. Welcome!

21 Jan 2022
Our paper "Evolutionary Variational Optimization of Generative Models" (Drefs et al.) has been accepted for publication by the Journal of Machine Learning Research (see here).

Machine Learning

Machine Learning

Machine Learning

Our Research

Based on first theoretical principles, our group develops novel efficient learning algorithms for standard and novel data models. The resulting algorithms are applied to a range of different domains including acoustic data, visual data, medical data and data of general pattern recognition tasks. Alongside the theoretical and practical algorithm development, we investigate advanced Machine Learning methods as models for neural information processing; and, visa versa, use ideas and insights from the neurosciences to motivate novel research directions in Machine Learning.

We pursue and conduct projects on efficient generative models (including deep generative models) for large-scale unsupervised and semi-supervised learning, autonomous learning and data enhancement.

We are part of the cluster of excellence Hearing4all and the Department of Medical Physics and Acoustics at the School of Medicine and Health Sciences.

For any inquiries please contact Jörg Lücke.

Selected and Recent Publications

(The complete list can be found here.)

D. Velychko, S. Damm, Z. Dai, A. Fischer and J. Lücke (2024).
Learning Sparse Codes with Entropy-Based ELBOs.
Int. Conf. on Artificial Intelligence and Statistics (AISTATS), 2024, accepted. (preprint, bibtex)

H. Mousavi, J. Drefs, F. Hirschberger, J. Lücke (2023).
Generic Unsupervised Optimization for a Latent Variable Model with Exponential Family Observables.
Journal of Machine Learning Research 24(285):1−59. (online access, bibtex)

S. Damm*, D. Forster, D. Velychko, Z. Dai, A. Fischer and J. Lücke* (2023).
The ELBO of Variational Autoencoders Converges to a Sum of Entropies.
Int. Conf. on Artificial Intelligence and Statistics (AISTATS), 3931-3960, 2023. (online access, bibtex).
*joint main contributions

J. Drefs*, E. Guiraud*, F. Panagiotou, J. Lücke (2023).
Direct Evolutionary Optimization of Variational Autoencoders With Binary Latents.
European Conference on Machine Learning, 357-372. (pdf, bibtex).
*joint first authorship

F. Hirschberger*, D. Forster* and J. Lücke (2022).
A Variational EM Acceleration for Efficient Clustering at Very Large Scales.
IEEE Transactions on Pattern Analysis and Machine Intelligence 44(12):9787-9801 (online access, bibtex).
*joint first authorship.

J. Drefs, E. Guiraud and J. Lücke (2022).
Evolutionary Variational Optimization of Generative Models.
Journal of Machine Learning Research 23(21):1-51 (online access, bibtex).

J. Lücke and D. Forster (2019).
k-means as a variational EM approximation of Gaussian mixture models.
Pattern Recognition Letters 125:349-356 (online access, bibtexarXiv)

A. S. Sheikh*, N. S. Harper*, J. Drefs, Y. Singer, Z. Dai, R.E. Turner and J. Lücke (2019).
STRFs in primary auditory cortex emerge from masking-based statistics of natural sounds.
PLOS Computational Biology 15(1): e1006595 (online access, bibtex)
*joint first authorship.

T. Monk, C. Savin and J. Lücke (2018).
Optimal neural inference of stimulus intensities.
Scientific Reports 8: 10038 (online access, bibtex)

A.-S. Sheikh and J. Lücke (2016).
Select-and-Sample for Spike-and-Slab Sparse Coding.
Advances in Neural Information Processing Systems (NIPS) 29: 3934-3942. (online accessbibtex)

Z. Dai and J. Lücke (2014).
Autonomous Document Cleaning – A Generative Approach to Reconstruct Strongly Corrupted Scanned Texts.
IEEE Transactions on Pattern Analysis and Machine Intelligence 36(10): 1950-1962. (online access, bibtex)

A.-S. Sheikh, J. A. Shelton, J. Lücke (2014).
A Truncated EM Approach for Spike-and-Slab Sparse Coding.
Journal of Machine Learning Research 15:2653-2687. (online access, bibtex)

COPYRIGHT NOTICE

The papers listed above have been published after peer review in different journals or conference proceedings. These journals or proceedings remain the only definitive repository of the content. Copyright and all rights therein are usually retained by the respective publishers. These materials may not be copied or reposted without their explicit permission. Use for scholarly purposes only.

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