2023W

• Bachelor Thesis in Computer Science / 194.112 / PR
• CAIML seminar / 192.138 / SE
• Generative AI / 194.154 / VU
• Interdisciplinary Project in Data Science / 194.147 / PR
• Introduction to Machine Learning / 194.025 / VU
• Journal Club "Solid State Physics: Theory and Experiment" / 194.138 / SE
• Machine Learning / 184.702 / VU
• Machine Learning Algorithms and Applications / 194.101 / PR
• Modern Aspects of Renormalization in Field Theories / 194.142 / UE
• Modern Aspects of Renormalization in Field Theories / 194.141 / VO
• Privatissimum for Doctor's Thesis / 194.028 / PV
• Project in Computer Science 1 / 194.145 / PR
• Project in Computer Science 2 / 194.146 / PR
• Scientific Research and Writing / 193.052 / SE
• Seminar for PhD Students / 194.110 / SE
• Seminar in Artificial Intelligence - Theoretical Aspects of Machine Learning / 194.118 / SE
• Theoretical Foundations and Research Topics in Machine Learning / 194.100 / VU

2024S

• Machine Learning / 184.702 / VU

• Structured Data Learning with Generalized Similarities
  2023 – 2027 / Vienna Science and Technology Fund (WWTF)
  Publications: 188226 / 188931 / 188939 / 188937
• Using single atom catalysts as nanozymes in FET sensors FET
  2023 – 2026 / Austrian Science Fund (FWF)
• Reliable reinforcement learning for sustainable enery systems
  2023 – 2025 / Austrian Research Promotion Agency (FFG)
• Modelling Complex Structured Real Biological and Chemical Data using MachineLearning
  2022 – 2023 / Austrian Exchange Service (OeAD)
• Artificial Intelligence for Advanced SAR Processing
  2021 – 2023 / Austrian Research Promotion Agency (FFG)

2023

• ModelRevelator: Fast phylogenetic model estimation via deep learning / Burgstaller-Muehlbacher, S., Crotty, S., Schmidt, H., Reden, F., Drucks, T., & von Haeseler, A. (2023). ModelRevelator: Fast phylogenetic model estimation via deep learning. Molecular Phylogenetics and Evolution, 188, Article 107905. https://doi.org/10.1016/j.ympev.2023.107905
  Download: PDF (5.18 MB)
• Correlations on all length scales in the Hubbard model and beyond / Andergassen, S. (2023, September 21). Correlations on all length scales  in the Hubbard model and beyond [Conference Presentation]. PHD-WORKSHOP FOR 5413, Germany.
• fRG analysis of the extended Hubbard model - SBE fluctuation diagnostics of screening / Al-Eryani, A., Heinzelmann, S., & Andergassen, S. (2023, September 18). fRG analysis of the extended Hubbard model - SBE fluctuation diagnostics of screening [Poster Presentation]. Autumn School on Correlated Electrons: Orbital Physics in Correlated Matter, Germany.
• Modular fRG Code for Lattice Fermion Systems: Challenges, Strategies and Ideas / Al-Eryani, A., & Andergassen, S. (2023, September 6). Modular fRG Code for Lattice Fermion Systems: Challenges, Strategies and Ideas [Conference Presentation]. numErical MEthods for fRG in condENsed maTter, Germany.
• Single-boson-exchange functional renormalization group and its application to the Hubbard model / Fraboulet, K., & Andergassen, S. (2023, September 6). Single-boson-exchange functional renormalization group and its application to the Hubbard model [Conference Presentation]. numErical MEthods for fRG in condENsed maTter, Germany.
• Single Boson Exchange fRG for Extended Hubbard Interactions / Al-Eryani, A., Scherer, M., & Andergassen, S. (2023, September 5). Single Boson Exchange fRG for Extended Hubbard Interactions [Poster Presentation]. numErical MEthods for fRG in condENsed maTter, Germany.
• Functional Renormalization Group Analysis of the Pseudogap Opening in the Hubbard Model / Patricolo, M., & Andergassen, S. (2023, September 5). Functional Renormalization Group Analysis of the Pseudogap Opening in the Hubbard Model [Poster Presentation]. numErical MEthods for fRG in condENsed maTter, Germany.
• Cluster Extension of DMF²RG / Krämer, M., & Andergassen, S. (2023, September 5). Cluster Extension of DMF2RG [Poster Presentation]. numErical MEthods for fRG in condENsed maTter, Germany.
• Efficient fRG flow equations for extended interactions and an application to the square and triangular lattices / Al-Eryani, A., Heinzelmann, S., Fraboulet, K., Scherer, M., & Andergassen, S. (2023, July 24). Efficient fRG flow equations for extended interactions and an application to the square and triangular lattices [Poster Presentation]. Workshop “Correlations in Novel Quantum Materials” 2023, Germany.
• The Mott metal-insulator transition in the two-dimensional Hubbard model - a cellular dynamical mean-field study on large clusters / Meixner, M., Klett, M., Heinzelmann, S., Wentzell, N., Hansmann, P., Andergassen, S., & Schäfer, T. (2023, July 24). The Mott metal-insulator transition in the two-dimensional Hubbard model - a cellular dynamical mean-field study on large clusters [Poster Presentation]. Workshop “Correlations in Novel Quantum Materials” 2023, Germany. http://hdl.handle.net/20.500.12708/187908
• Single-boson-exchange functional renormalization group and its application to the Hubbard model / Fraboulet, K., Al-Eryani, A., Andergassen, S., & Heinzelmann, S. (2023, July 24). Single-boson-exchange functional renormalization group and its application to the Hubbard model [Poster Presentation]. Workshop “Correlations in Novel Quantum Materials” 2023, Germany.
• Pseudogap opening in the Hubbard model at strong coupling / Patricolo, M., Vilardi, D., Heinzelmann, S., Andergassen, S., & Bonetti, V. (2023, July 24). Pseudogap opening in the Hubbard model at strong coupling [Poster Presentation]. Workshop “Correlations in Novel Quantum Materials” 2023, Germany.
• Cluster extension of DMF2RG / Krämer, M., Fraboulet, K., Vilardi, D., Bonetti, P. M., Meixner, M., Schäfer, T., & Andergassen, S. (2023, July 24). Cluster extension of DMF2RG [Poster Presentation]. Workshop “Correlations in Novel Quantum Materials” 2023, Stuttgart, Germany.
• A New Aligned Simple German Corpus / Toborek, V., Busch, M., Boßert, M., Bauckhage, C., & Welke, P. (2023). A New Aligned Simple German Corpus. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (pp. 11393–11412). Association for Computational Linguistics. https://doi.org/10.18653/v1/2023.acl-long.638
  Project: StruDL (2023–2027)
• Hidden Schema Networks / Sanchez, R., Conrads, L., Welke, P., Cvejoski, K., & Ojeda, C. (2023). Hidden Schema Networks. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (pp. 4764–4798). Association for Computational Linguistics. https://doi.org/10.18653/v1/2023.acl-long.263
  Project: StruDL (2023–2027)
• Machine learning dynamics of quantum systems / Andergassen, S. (2023, May 8). Machine learning dynamics of quantum systems [Presentation]. TACO Colloquium, Austria.
• Pseudogap opening in the Hubbard model at strong coupling / Patricolo, M., Heinzelmann, S., Bonetti, P. M., Vilardi, D., & Andergassen, S. (2023, May 4). Pseudogap opening in the Hubbard model at strong coupling [Poster Presentation]. Exploring New Topics with Functional Renormalisation, Germany.
• Machine learning dynamics of quantum systems / Andergassen, S. (2023, April 13). Machine learning dynamics of quantum systems [Conference Presentation]. Machine Learning and (Quantum) Physics Workshop 2023, Obergurgl, Austria.
• Krein support vector machine classification of antimicrobial peptides / Redshaw, J., Ting, D. S. J., Brown, A., Hirst, J. D., & Gärtner, T. (2023). Krein support vector machine classification of antimicrobial peptides. Digital Discovery. https://doi.org/10.1039/D3DD00004D
• Deep Symbolic Learning: Discovering Symbols and Rules from Perceptions / Daniele, A., Campari, T., Malhotra, S., & Serafini, L. (2023). Deep Symbolic Learning: Discovering Symbols and Rules from Perceptions. In Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence (pp. 3597–3605). https://doi.org/10.24963/ijcai.2023/400
• Retention is All You Need / Mohiuddin, K., Alam, M. A., Alam, M. M., Welke, P., Martin, M., Lehmann, J., & Vahdati, S. (2023). Retention is All You Need. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management (pp. 4752–4758). https://doi.org/10.1145/3583780.3615497
• An Empirical Evaluation of the Rashomon Effect in Explainable Machine Learning / Müller, S., Toborek, V., Beckh, K., Jakobs, M., Bauckhage, C., & Welke, P. (2023). An Empirical Evaluation of the Rashomon Effect in Explainable Machine Learning. In D. Koutra, C. Plant, M. Gomez Rodriguez, E. Baralis, & F. Bonchi (Eds.), Machine Learning and Knowledge Discovery in Databases: Research Track : European Conference, ECML PKDD 2023, Turin, Italy, September 18–22, 2023, Proceedings, Part III (pp. 462–478). Springer. https://doi.org/10.1007/978-3-031-43418-1_28
  Project: StruDL (2023–2027)
• Expectation-Complete Graph Representations with Homomorphisms / Welke, P., Thiessen, M., Jogl, F., & Gärtner, T. (2023). Expectation-Complete Graph Representations with Homomorphisms. In A. Krause, E. Brunskill, K. Cho, B. Engelhardt, S. Sabato, & J. Scarlett (Eds.), Proceedings of the 40th International Conference on Machine Learning (pp. 36910–36925). Proceedings of Machine Learning Research.
  Project: StruDL (2023–2027)

2022

• Generalized Laplacian Positional Encoding for Graph Representation Learning / Maskey, S., Parviz, A., Thiessen, M., Stärk, H., Sadikaj, Y., & Maron, H. (2022, December 3). Generalized Laplacian Positional Encoding for Graph Representation Learning [Poster Presentation]. NeurIPS 2022 Workshop on Symmetry and Geometry in Neural Representations, New Orleans, United States of America (the). https://doi.org/10.34726/3908
  Download: Paper (1.74 MB)
• Expectation Complete Graph Representations Using Graph Homomorphisms / Welke, P., Thiessen, M., & Gärtner, T. (2022, November 30). Expectation Complete Graph Representations Using Graph Homomorphisms [Poster Presentation]. First Learning on Graphs Conference (LoG 2022), International. https://doi.org/10.34726/3883
  Download: Accepted Paper (294 KB)
• Active Learning of Classifiers with Label and Seed Queries / Bressan, M., Cesa-Bianchi, N., Lattanzi, S., Paudice, A., & Thiessen, M. (2022). Active Learning of Classifiers with Label and Seed Queries. In Advances in Neural Information Processing Systems 35 (NeurIPS 2022). Thirty-Sixth Conference on Neural Information Processing Systems (NeurIPS 2022), New Orleans, Louisiana, United States of America (the). Neural information processing systems foundation. https://doi.org/10.34726/4021
  Downloads: Full paper (387 KB) / Supplemantary material (294 KB)
• LieGG: Studying Learned Lie Group Generators / Moskalev, A., Sepliarskaia, A., Sosnovik, I., & Smeulders, A. (2022). LieGG: Studying Learned Lie Group Generators. In Advances in Neural Information Processing Systems 35 (NeurIPS 2022). Advances in Neural Information Processing Systems 35 (NeurIPS 2022), New Orleans, United States of America (the).
• Expectation Complete Graph Representations using Graph Homomorphisms / Thiessen, M., Pascal Welke, & Gärtner, T. (2022, October 25). Expectation Complete Graph Representations using Graph Homomorphisms [Presentation]. Workshop: Hot Topics in Graph Neural Networks, Kassel, Germany.
  Download: slides of invited talk (1.26 MB)
• Expectation Complete Graph Representations Using Graph Homomorphisms / Thiessen, M., Welke, P., & Gärtner, T. (2022, October 21). Expectation Complete Graph Representations Using Graph Homomorphisms [Poster Presentation]. New Frontiers in Graph Learning (GLFrontiers) NeurIPS 2022 Workshop, New Orleans, United States of America (the). https://doi.org/10.34726/3863
  Download: Full paper (304 KB)
• Weisfeiler and Leman Return with Graph Transformations / Jogl, F., Thiessen, M., & Gärtner, T. (2022). Weisfeiler and Leman Return with Graph Transformations. In 18th International Workshop on Mining and Learning with Graphs - Accepted Papers. 18th International Workshop on Mining and Learning with Graphs, Grenoble, France. https://doi.org/10.34726/3829
  Download: Full paper as PDF (439 KB)
• Unlearning Protected User Attributes in Recommendations with Adversarial Training / Ganhör, C., Penz, D., Rekabsaz, N., Lesota, O., & Schedl, M. (2022). Unlearning Protected User Attributes in Recommendations with Adversarial Training. In SIGIR ’22: Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 2142–2147). https://doi.org/10.1145/3477495.3531820
• EmoMTB: Emotion-aware Music Tower Blocks / Melchiorre, A. B., Penz, D., Ganhör, C., Lesota, O., Fragoso, V., Friztl, F., Parada-Cabaleiro, E., Schubert, F., & Schedl, M. (2022). EmoMTB: Emotion-aware Music Tower Blocks. In ICMR ’22: Proceedings of the 2022 International Conference on Multimedia Retrieval (pp. 206–210). https://doi.org/10.1145/3512527.3531351
• Reducing Learning on Cell Complexes to Graphs / Jogl, F., Thiessen, M., & Gärtner, T. (2022). Reducing Learning on Cell Complexes to Graphs. In ICLR 2022 Workshop on Geometrical and Topological Representation Learning. ICLR 2022 Workshop on Geometrical and Topological Representation Learning, International. https://doi.org/10.34726/3421
  Download: Paper as PDF (263 KB)
• Dojo: A Benchmark for Large Scale Multi-Task Reinforcement Learning / Schmidt, D. (2022). Dojo: A Benchmark for Large Scale Multi-Task Reinforcement Learning. In ALOE 2022. Accepted Papers. Workshop on Agent Learning in Open-Endedness (ALOE) at ICLR 2022, International. https://doi.org/10.34726/4263
  Download: PDF (5 MB)
• Kernel Methods for Predicting Yields of Chemical Reactions / Haywood, A. L., Redshaw, J., Hanson-Heine, M. W. D., Taylor, A., Brown, A., Mason, A. M., Gärtner, T., & Hirst, J. D. (2022). Kernel Methods for Predicting Yields of Chemical Reactions. Journal of Chemical Information and Modeling, 62(9), 2077–2092. https://doi.org/10.1021/acs.jcim.1c00699
• One-Shot Learning of Ensembles of Temporal Logic Formulas for Anomaly Detection in Cyber-Physical Systems / Indri, P., Bartoli, A., Medvet, E., & Nenzi, L. (2022). One-Shot Learning of Ensembles of Temporal Logic Formulas for Anomaly Detection in Cyber-Physical Systems. In EuroGP 2022: Genetic Programming (pp. 34–50). Springer-Verlag. https://doi.org/10.1007/978-3-031-02056-8_3
• Online learning of convex sets on graphs / Thiessen, M., & Gärtner, T. (2022). Online learning of convex sets on graphs. In Joint European Conference on Machine Learning and Knowledge Discovery in Databases. Joint European Conference on Machine Learning and Knowledge Discovery in Databases (ECML PKDD 2022), Grenoble, France.
• LFM-2b: A Dataset of Enriched Music Listening Events for Recommender Systems Research and Fairness Analysis / Schedl, M., Brandl, S., Lesota, O., Parada-Cabaleiro, E., Penz, D., & Rekabsaz, N. (2022). LFM-2b: A Dataset of Enriched Music Listening Events for Recommender Systems Research and Fairness Analysis. In ACM SIGIR Conference on Human Information Interaction and Retrieval. ACM. https://doi.org/10.1145/3498366.3505791

2021

• Fast and Data-Efficient Training of Rainbow: an Experimental Study on Atari / Schmidt, D., & Schmied, T. (2021, December 13). Fast and Data-Efficient Training of Rainbow: an Experimental Study on Atari [Poster Presentation]. Deep RL Workshop NeurIPS 2021, Online, International. https://doi.org/10.34726/3909
  Download: Presentation Paper (5.87 MB)
• Active Learning Convex Halfspaces on Graphs / Thiessen, M., & Gärtner, T. (2021). Active Learning Convex Halfspaces on Graphs. In SubSetML @ ICML2021: Subset Selection in Machine Learning: From Theory to Practice. SubSetML: Subset Selection in Machine Learning: From Theory to Practice, International. https://doi.org/10.34726/3901
  Download: Accepted full paper with appendix (2.27 MB)
• Active Learning of Convex Halfspaces on Graphs / Thiessen, M., & Gärtner, T. (2021). Active Learning of Convex Halfspaces on Graphs. In Advances in Neural Information Processing Systems 34 (NeurIPS 2021) (pp. 1–13). https://doi.org/10.34726/1841
  Download: PDF (1.06 MB)
• Team JKU-AIWarriors in the ACM Recommender Systems Challenge 2021: Lightweight XGBoost Recommendation Approach Leveraging User Features / Krauck, A., Penz, D., & Schedl, M. (2021). Team JKU-AIWarriors in the ACM Recommender Systems Challenge 2021: Lightweight XGBoost Recommendation Approach Leveraging User Features. In RecSysChallenge ’21: Proceedings of the Recommender Systems Challenge 2021. ACM. https://doi.org/10.1145/3487572.3487874
• Active Learning of Convex Halfspaces on Graphs / Thiessen, M., & Gärtner, T. (2021). Active Learning of Convex Halfspaces on Graphs. In Advances in Neural Information Processing Systems 34. Advances in Neural Information Processing Systems 34. http://hdl.handle.net/20.500.12708/58787

2020

• Efficient Reinforcement Learning via Self-supervised learning and Model-based methods / Schmied, T., & Thiessen, M. (2020). Efficient Reinforcement Learning via Self-supervised learning and Model-based methods. In Challenges of Real-World RL. NeurIPS 2020 Workshop. Accepted Papers. 34th Conference on Neural Information Processing Systems (NeurIPS 2020), Vancouver, Canada. https://doi.org/10.34726/4524
  Download: Accepted paper (236 KB)
• Active Learning on Graphs with Geodesically Convex Classes / Thiessen, M., & Gärtner, T. (2020). Active Learning on Graphs with Geodesically Convex Classes. In Proceedings of 16th International Workshop on Mining and Learning with Graphs (MLG’20). 16th International Workshop on Mining and Learning with Graphs, Austria. https://doi.org/10.34726/3467
  Download: author's original (729 KB)
• Machine Learning for Chemical Synthesis / Haywood, A. L., Redshaw, J., Gärtner, T., Taylor, A., Mason, A. M., & Hirst, J. D. (2020). Machine Learning for Chemical Synthesis. In H. M. Cartwright (Ed.), Machine Learning in Chemistry : The Impact of Artificial Intelligence (pp. 169–194). The Royal Society of Chemistry. https://doi.org/10.1039/9781839160233-00169

• On SGD with momentum / Plattner, M. (2023). On SGD with momentum [Diploma Thesis, Technische Universität Wien]. reposiTUm. https://doi.org/10.34726/hss.2023.106165
  Download: PDF (1.69 MB)
• Reinforcement learning for games with imperfect information – Teaching an agent the game of Schnapsen / Salzer, T. (2023). Reinforcement learning for games with imperfect information – Teaching an agent the game of Schnapsen [Diploma Thesis, Technische Universität Wien]. reposiTUm. https://doi.org/10.34726/hss.2023.86961
  Download: PDF (1.2 MB)
• Antibody-Antigen Binding Affinity Prediction through the use of geometric deep learning / Traxler, F. (2023). Antibody-Antigen Binding Affinity Prediction through the use of geometric deep learning [Diploma Thesis, Technische Universität Wien]. reposiTUm. https://doi.org/10.34726/hss.2023.105645
  Download: PDF (4.87 MB)
• Do we need to Improve message passing? Improving graph neural networks with graph transformations / Jogl, F. (2022). Do we need to Improve message passing? Improving graph neural networks with graph transformations [Diploma Thesis, Technische Universität Wien]. reposiTUm. https://doi.org/10.34726/hss.2022.103141
  Download: PDF (897 KB)
• Self-supervision, data augmentation and online fine-tuning for offline RL / Schmied, T. (2022). Self-supervision, data augmentation and online fine-tuning for offline RL [Diploma Thesis, Technische Universität Wien]. reposiTUm. https://doi.org/10.34726/hss.2022.89725
  Download: PDF (4.85 MB)
• Representation learning for variable-sized multiple sequence alignments / Drucks, T. (2021). Representation learning for variable-sized multiple sequence alignments [Diploma Thesis, Technische Universität Wien]. reposiTUm. https://doi.org/10.34726/hss.2021.88083
  Download: PDF (24.3 MB)
• Recommending reviewers for theses using artificial intelligence / Penz, D. (2021). Recommending reviewers for theses using artificial intelligence [Diploma Thesis, Technische Universität Wien]. reposiTUm. https://doi.org/10.34726/hss.2021.76463
  Download: PDF (2.07 MB)

• Clemens Heitzinger: PDE Models for Nanotechnology
  2013 / START Prize / Austria / Website
• Clemens Heitzinger: START-Preis
  2013 / START-Programm / Austria
• Clemens Heitzinger: Erwin Schrödinger Fellowship (FWF)
  2003 / Schrödinger-Stipendium / Austria

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