Prof. Dr. Yannic Noller

Curriculum Vitae

Before joining RUB in July 2024, Yannic was an Assistant Professor at Singapore University of Technology and Design (SUTD) and a Research Assistant Professor in the Department of Computer Science at the National University of Singapore (NUS). He pursued his Ph.D. in Computer Science in the Software Engineering group (advised by Prof. Lars Grunske) at the Humboldt-Universität zu Berlin, Germany. His Ph.D. research focused on differential software testing, in particular, by combining fuzzing and symbolic execution in the context of regression analysis, algorithmic complexity analysis, side-channel analysis, and robustness analysis of neural networks.

Research

Yannic’s research focuses on how software quality can be maintained and improved with automated testing and repair technologies. His general research goal is to shape the future of software development by contributing to the domain of automated software engineering and providing the means to develop reliable, trustworthy, and secure software systems. In particular, he works in the following areas:

Automated Program Repair: developing new repair techniques to aid developers in fixing program bugs
Machine Learning Analysis: automated analysis, testing, and repairing of machine learning models
Software Testing: exploring and designing (hybrid) testing techniques to systematically generate test inputs that expose incorrect program behavior
Intelligent Tutoring Systems: how to help CS students learn programming by applying concepts from automated testing and repair to guide the students toward the right solution
Human Factors in SE: studying developer needs and requirements for successful deployment of testing and repair techniques in development practice

More information can be found on Yannic’s personal website: https://yannicnoller.github.io/

Publications

2024

[1]

J. Ricker, S. Damm, T. Holz, und A. Fischer, „Towards the detection of diffusion model deepfakes“, in Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications – (Volume 4), Rom, März 2024, S. 446–457. doi: 10.5220/0012422000003660.

[2]

S. Däubener, A. Fischer, T. Glasmachers, und S. Mandt, „Leveraging stochasticity to increase the robustness of artificial neural networks“, Universitätsbibliothek, Ruhr-Universität Bochum, Bochum, 2024. doi: 10.13154/294-12582.

[3]

J. Ricker, D. Assenmacher, T. Holz, A. Fischer, und E. Quiring, „AI-generated faces in the real world: a large-scale case study of twitter profile images“, in Proceedings of the 27th international symposium on research in attacks, intrusions and defenses, Padua, Italien, Sep. 2024, S. 513–530. doi: 10.1145/3678890.3678922.

[4]

S. Mavali, J. Ricker, D. Pape, Y. Sharma, A. Fischer, und L. Schönherr, „Fake it until you break it: on the adversarial robustness of AI-generated image detectors“, 2. Oktober 2024. https://arxiv.org/abs/2410.01574

[5]

M. Laszkiewicz, J. Ricker, J. Lederer, und A. Fischer, „Single-model attribution of generative models through final-layer inversion“, in Proceedings of the 41st International Conference on Machine Learning (ICML 2024), 2024, Bd. 235, S. 26007–26042. [Online]. Verfügbar unter: https://raw.githubusercontent.com/mlresearch/v235/main/assets/laszkiewicz24a/laszkiewicz24a.pdf

[6]

J. Ricker, D. Lukovnikov, und A. Fischer, „AEROBLADE: training-free detection of latent diffusion images using autoencoder reconstruction error“, 31. Januar 2024.

[7]

J. Ricker, D. Lukovnikov, und A. Fischer, „AEROBLADE: training-free detection of latent diffusion images using autoencoder reconstruction error“, in Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, Sep. 2024, Bd. 2024. doi: 10.1109/cvpr52733.2024.00872.

[8]

J. Frank u. a., „A representative study on human detection of artificially generated media across countries“, in 2024 IEEE Symposium on Security and Privacy (SP), San Francisco, Sep. 2024, S. 55–73. doi: 10.1109/sp54263.2024.00159.

2023

[1]

M.-R. Amini, S. Canu, A. Fischer, T. Guns, P. K. Novak, und G. Tsoumakas, Hrsg., Machine learning and knowledge discovery in databases, Part IV: European conference, ECML PKDD 2022, Grenoble, France, September 19–23, 2022, proceedings. Cham: Springer Cham, 2023. doi: 10.1007/978-3-031-26412-2.

[2]

K. Maag und A. Fischer, „Uncertainty-weighted loss functions for improved adversarial attacks on semantic segmentation“, 22. Mai 2023.

[3]

K. Maag und A. Fischer, „Uncertainty-weighted Loss Functions for Improved Adversarial Attacks on Semantic Segmentation“, 26. Oktober 2023.

[4]

M. Laszkiewicz, J. Ricker, J. Lederer, und A. Fischer, „Single-model attribution of generative models through final-layer inversion“, 26. Mai 2023.

[5]

J. Frank u. a., „A representative study on human detection of artificially generated media across countries“, 10. Dezember 2023.

[6]

M.-R. Amini, S. Canu, A. Fischer, T. Guns, P. Kralj Novak, und G. Tsoumakas, Hrsg., Machine Learning and Knowledge Discovery in Databases. Cham: Springer Nature Switzerland, 2023. doi: 10.1007/978-3-031-26422-1.

2022

[1]

S. Däubener und A. Fischer, „How sampling impacts the robustness of stochastic neural networks“, Apr. 2022. doi: 10.48550/arxiv.2204.10839.

[2]

N. Zengeler, T. Glasmachers, A. Fischer, und U. Handmann, „Transfer meta learning: Herausforderungen der Mustererkennung“, Universitätsbibliothek, Ruhr-Universität Bochum, Bochum, 2022. doi: 10.13154/294-9296.

[3]

F. Linsner, L. Adilova, S. Däubener, M. Kamp, und A. Fischer, „Approaches to uncertainty quantification in federated deep learning“, in Machine learning and principles and practice of knowledge discovery in databases, online, Feb. 2022, Bd. 1524, S. 128–145. doi: 10.1007/978-3-030-93736-2_12.

[4]

J. Ricker, S. Damm, T. Holz, und A. Fischer, „Towards the detection of diffusion model deepfakes“, 26. Oktober 2022. https://arxiv.org/abs/2210.14571

2021

[1]

K. Brügge, A. Fischer, und C. Igel, „On the convergence of the Metropolis algorithm with fixed-order updates for multivariate binary probability distributions“, in International Conference on Artificial Intelligence and Statistics, Online, 2021, Bd. 130, S. 469–477. [Online]. Verfügbar unter: https://proceedings.mlr.press/v130/brugge21a.html

[2]

D. Lukovnikov, S. Däubener, und A. Fischer, „Detecting compositionally out-of-distribution examples in semantic parsing“, in Findings of the association for computational linguistics – findings of ACL: EMNLP 2021, Dez. 2021, S. 591–598. doi: 10.18653/v1/2021.findings-emnlp.54.

[3]

A. P. Raulf, S. Däubener, B. L. Hack, A. Mosig, und A. Fischer, „SmoothLRP: smoothing LRP by averaging over stochastic input variations“, in 29th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, online, Okt. 2021, S. 599–604. doi: 10.14428/esann/2021.es2021-139.

2020

[1]

J. Frank, T. Eisenhofer, L. Schönherr, A. Fischer, D. Kolossa, und T. Holz, „Leveraging frequency analysis for deep fake image recognition“, 26. Juni 2020. https://arxiv.org/pdf/2003.08685.pdf

[2]

S. Däubener, L. Schönherr, A. Fischer, und D. Kolossa, „Detecting adversarial examples for speech recognition via uncertainty quantification“, in Cognitive intelligence for speech processing, Okt. 2020, S. 4661–4665. doi: 10.21437/interspeech.2020-2734.

[3]

J. Frank, T. Eisenhofer, L. Schönherr, A. Fischer, D. Kolossa, und T. Holz, „Leveraging frequency analysis for deep fake image recognition“, in 37th International Conference on Machine Learning (ICML 2020), Online, 2020, Bd. 119, S. 3205–3216.

[4]

S. Däubener, J. Frank, T. Holz, und A. Fischer, „Efficient calculation of adversarial examples for bayesian neural networks “, 2020. [Online]. Verfügbar unter: https://openreview.net/pdf?id=0KlHSyXxHDW

2019

[1]

A. Kristiadi, S. Däubener, und A. Fischer, „Uncertainty quantification with compound density networks“, gehalten auf der Conference on Neural Information Processing Systems (NeurIPS), Vancouver, 13. Dezember 2019, Publiziert. [Online]. Verfügbar unter: https://casa.rub.de/en/research/publications/detail/uncertainty-quantification-with-compound-density-network-workshop-beitrag

[2]

S. Däubener, A. Kristiadi, und A. Fischer, „Infinite ensembles for uncertainty prediction“, gehalten auf der Women in Machine Learning, Vancouver, 9. Dezember 2019, Publiziert. doi: 10.48550/arxiv.1902.01080.

[3]

A. Kristiadi, S. Däubener, und A. Fischer, „uncertainty quantification with compound density networks“, Konferenz-Abstract, 2019. [Online]. Verfügbar unter: http://bayesiandeeplearning.org/2019/papers/57.pdf

2018

[1]

B. Weghenkel, A. Fischer, und L. Wiskott, „Graph-based predictable feature analysis“, Machine learning, Bd. 106, Nr. 9–10, S. 1359–1380, Mai 2017, doi: 10.1007/s10994-017-5632-x.

2017

[1]B. Weghenkel, A. Fischer, und L. Wiskott, „Graph-based predictable feature analysis“, Machine learning, Bd. 106, Nr. 9–10, S. 1359–1380, Mai 2017, doi: 10.1007/s10994-017-5632-x.

2016

[1]

J. Melchior, A. Fischer, und L. Wiskott, „How to center deep Boltzmann machines“, Journal of machine learning research, Bd. 17, Art. Nr. 99, 2016.

2015

[1]

A. Fischer und C. Igel, „A bound for the convergence rate of parallel tempering for sampling restricted Boltzmann machines“, Theoretical computer science, Bd. 598, S. 102–117, 2015, doi: 10.1016/j.tcs.2015.05.019.

2014

[1]

A. Fischer und C. Igel, „Training restricted Boltzmann machines: An introduction“, Pattern recognition, Bd. 47, Nr. 1, S. 25–39, 2014, doi: 10.1016/j.patcog.2013.05.025.

2013

[1]

K. Brügge, A. Fischer, und C. Igel, „The flip-the-state transition operator for restricted Boltzmann machines“, Machine learning, Bd. 93, Nr. 1, S. 53–69, 2013, doi: 10.1007/s10994-013-5390-3.

[2]

J. Melchior, A. Fischer, N. Wang, und L. Wiskott, „How to center binary Restricted Boltzmann Machines“, 2013. [Online]. Verfügbar unter: http://arxiv.org/pdf/1311.1354v3.pdf

[3]

O. Krause, A. Fischer, T. Glasmachers, und C. Igel, „Approximation properties of DBNs with binary hidden units and real-valued visible units“, in 30th International Conference on Machine Learning, Atlanta, 2013, Bd. 28, S. 419–426.

2012

[1]

A. Fischer und C. Igel, „An introduction to restricted Boltzmann machines“, in Progress in pattern recognition, image analysis, computer vision, and applications, Buenos Aires, 2012, Bd. 7441, S. 14–36. doi: 10.1007/978-3-642-33275-3_2.

2011

[1]

A. Fischer und C. Igel, „Bounding the bias of contrastive divergence learning“, Neural computation, Bd. 23, Nr. 3, S. 664–673, 2011, doi: 10.1162/neco_a_00085.

2010

[1]

A. Fischer und C. Igel, „Empirical analysis of the divergence of Gibbs sampling based learning algorithms for Restricted Boltzmann Machines“, in Artificial neural networks – ICANN 2010, Thessaloniki, 2010, Bd. 6352–6354, S. 208–217. doi: 10.1007/978-3-642-15825-4_26.