@inproceedings{chatterjee2025value,
  title={Value Iteration with Guessing for Markov Chains and Markov Decision Processes},
  author={Chatterjee, Krishnendu and JafariRaviz, Mahdi and Saona, Raimundo and Svoboda, Jakub},
  booktitle={International Conference on Tools and Algorithms for the Construction and Analysis of Systems},
  pages={217--236},
  year={2025},
  organization={Springer}
}

@inproceedings{chatterjee2025refuting,
  title={Refuting Equivalence in Probabilistic Programs with Conditioning},
  author={Chatterjee, Krishnendu and Kafshdar Goharshady, Ehsan and Novotn{\`y}, Petr and {\v{Z}}ikeli{\'c}, {\DJ}or{\dj}e},
  booktitle={International Conference on Tools and Algorithms for the Construction and Analysis of Systems},
  pages={279--300},
  year={2025},
  organization={Springer}
}

@article{ekaputra2025pattern,
  title={Pattern-based engineering of Neurosymbolic AI Systems},
  author={Ekaputra, Fajar J},
  journal={Journal of Web Semantics},
  volume={85},
  pages={100855},
  year={2025},
  publisher={Elsevier}
}

@article{tsanevaknowledge,
  title={Knowledge Engineering with Large Language Models: A Capability Assessment in Ontology Evaluation},
  author={Tsaneva, Stefani and Herwanto, Guntur Budi and Llugiqi, Majlinda and Sabou, Marta}
}

@article{schimunek2025mhnfs,
  title={MHNfs: Prompting In-Context Bioactivity Predictions for Low-Data Drug Discovery},
  author={Schimunek, Johannes and Luukkonen, Sohvi and Klambauer, Günter},
  journal={Journal of Chemical Information and Modeling},
  year={2025},
  publisher={ACS Publications}
}

@inproceedings{hausberger2025exim,
  title={ExIM: Exploring Intent of Music Listening for Retrieving User-generated Playlists},
  author={Hausberger, Anna and Strauss, Hannah and Schedl, Markus},
  booktitle={Proceedings of the 2025 ACM SIGIR Conference on Human Information Interaction and Retrieval},
  pages={348--357},
  year={2025}
}

@article{chatterjee2025liquid,
  title={When is liquid democracy possible? On the manipulation of variance.},
  author={Chatterjee, Krishnendu and Gilbert, Seth and Schmid, Stefan and Svoboda, Jakub and Yeo, Michelle},
  journal={Cryptology ePrint Archive},
  year={2025}
}

@inproceedings{ahmetaj2025common,
  title={Common Foundations for SHACL, ShEx, and PG-Schema},
  author={Ahmetaj, Shqiponja and Boneva, Iovka and Hidders, Jan and Hose, Katja and Jakubowski, Maxime and Labra Gayo, Jose Emilio and Martens, Wim and Mogavero, Fabio and Murlak, Filip and Okulmus, Cem and others},
  booktitle={Proceedings of the ACM on Web Conference 2025},
  pages={8--21},
  year={2025}
}

@inproceedings{koroglu2025towards,
  title={Towards Improving Automated Testing with GraphWalker},
  author={Koroglu, Yavuz and Beyaz{\i}t, Mutlu and Kilincceker, Onur and Demeyer, Serge and Wotawa, Franz},
  booktitle={2025 IEEE International Conference on Software Testing, Verification and Validation Workshops (ICSTW)},
  pages={54--58},
  year={2025},
  organization={IEEE}
}

@article{
doi:10.1073/pnas.2423072122,
author = {Simone Bombari  and Marco Mondelli },
title = {Privacy for free in the overparameterized regime},
journal = {Proceedings of the National Academy of Sciences},
volume = {122},
number = {15},
pages = {e2423072122},
year = {2025},
doi = {10.1073/pnas.2423072122},
URL = {https://www.pnas.org/doi/abs/10.1073/pnas.2423072122},
eprint = {https://www.pnas.org/doi/pdf/10.1073/pnas.2423072122},
abstract = {In many deep learning applications, training datasets routinely include personal, sensitive information. Learning from these data is possible without creating privacy infringement via methods guaranteeing differential privacy, designed to provide provable protection to any individual user. However, differential privacy comes with a performance cost, and the cost is often believed to grow with the number of parameters of the learning model. Our work challenges this view, showing that overparameterization is not at odds with privacy. In fact, we prove that, for a class of overparameterized models having access to enough training samples, privacy even comes for free, i.e., with a small loss in performance. This result provides theoretical support to the development of differentially private models at scale. Differentially private gradient descent (DP-GD) is a popular algorithm to train deep learning models with provable guarantees on the privacy of the training data. In the last decade, the problem of understanding its performance cost with respect to standard GD has received remarkable attention from the research community, which has led to upper bounds on the excess population risk RP in different learning settings. However, such bounds typically degrade with overparameterization, i.e., as the number of parameters p gets larger than the number of training samples n—a regime which is ubiquitous in current deep-learning practice. As a result, the lack of theoretical insights leaves practitioners without clear guidance, leading some to reduce the effective number of trainable parameters to improve performance, while others use larger models to achieve better results through scale. In this work, we show that in the popular random features model with quadratic loss, for any sufficiently large p, privacy can be obtained for free, i.e., RP=o(1), not only when the privacy parameter ε has constant order but also in the strongly private setting ε=o(1). This challenges the common wisdom that overparameterization inherently hinders performance in private learning.}}
}

@inproceedings{adam2025asp,
  title={ASP-Driven Emergency Planning for Norm Violations in Reinforcement Learning},
  author={Adam, Sebastian and Eiter, Thomas},
  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
  volume={39},
  number={14},
  pages={14772--14780},
  year={2025}
}

@inproceedings{janota2025breaking,
  title={Breaking symmetries in quantified graph search: A comparative study},
  author={Janota, Mikol{\'a}{\v{s}} and Kirchweger, Markus and Peitl, Tom{\'a}{\v{s}} and Szeider, Stefan},
  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
  volume={39},
  number={11},
  pages={11246--11254},
  year={2025}
}

@inproceedings{ganian2025parameterized,
  title={Parameterized Complexity of Caching in Networks},
  author={Ganian, Robert and Mc Inerney, Fionn and Tsigkari, Dimitra},
  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
  volume={39},
  number={11},
  pages={11229--11237},
  year={2025}
}

@inproceedings{chatterjee2025quantified,
  title={Quantified Linear and Polynomial Arithmetic Satisfiability via Template-based Skolemization},
  author={Chatterjee, Krishnendu and Goharshady, Ehsan Kafshdar and Karrabi, Mehrdad and Motwani, Harshit J and Seeliger, Maximilian and {\v{Z}}ikeli{\'c}, {\DJ}or{\dj}e},
  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
  volume={39},
  number={11},
  pages={11158--11166},
  year={2025}
}

@inproceedings{deligkas2025complexity,
  title={The Complexity of Extending Fair Allocations of Indivisible Goods},
  author={Deligkas, Argyrios and Eiben, Eduard and Ganian, Robert and Goldsmith, Tiger-Lily and Ioannidis, Stavros D},
  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
  volume={39},
  number={13},
  pages={13745--13753},
  year={2025}
}

@inproceedings{chatterjee2025linear,
  title={Linear Equations with Min and Max Operators: Computational Complexity},
  author={Chatterjee, Krishnendu and Luo, Ruichen and Saona, Raimundo and Svoboda, Jakub},
  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
  volume={39},
  number={11},
  pages={11150--11157},
  year={2025}
}

@article{plank2025solution,
  title={Solution Counts of Some Prominent Quantified Boolean Formulas Families},
  author={Plank, Andreas and Kauers, Manuel and Seidl, Martina},
  year={2025}
}

@inproceedings{avarikioti2024route,
  title={Route discovery in private payment channel networks},
  author={Avarikioti, Zeta and Bastankhah, Mahsa and Maddah-Ali, Mohammad Ali and Pietrzak, Krzysztof and Svoboda, Jakub and Yeo, Michelle},
  booktitle={European Symposium on Research in Computer Security},
  pages={207--223},
  year={2024},
  organization={Springer}
}

@incollection{baldazzi2025knowledge,
  title={Knowledge Graph-Based Reasoning in Large Language Models},
  author={Baldazzi, Teodoro and Bellomarini, Luigi and Sallinger, Emanuel},
  booktitle={Handbook on Neurosymbolic AI and Knowledge Graphs},
  pages={441--465},
  year={2025},
  publisher={IOS Press}
}

@article{schweiger2025impact,
  title={The impact of playlist characteristics on coherence in user-curated music playlists},
  author={Schweiger, Harald and Parada-Cabaleiro, Emilia and Schedl, Markus},
  journal={EPJ Data Science},
  volume={14},
  number={1},
  pages={24},
  year={2025},
  publisher={Springer Berlin Heidelberg}
}

@inproceedings{ielanskyiaddressing,
  title={Addressing Pitfalls in the Evaluation of Uncertainty Estimation Methods for Natural Language Generation},
  author={Ielanskyi, Mykyta and Schweighofer, Kajetan and Aichberger, Lukas and Hochreiter, Sepp},
  booktitle={ICLR Workshop: Quantify Uncertainty and Hallucination in Foundation Models: The Next Frontier in Reliable AI}
}

@article{bergougnoux2023space,
  title={Space-efficient parameterized algorithms on graphs of low shrubdepth},
  author={Bergougnoux, Benjamin and Chekan, Vera and Ganian, Robert and Kant{\'e}, Mamadou Moustapha and Mnich, Matthias and Oum, Sang-il and Pilipczuk, Micha{\l} and van Leeuwen, Erik Jan},
  journal={ACM Transactions on Computation Theory},
  year={2023},
  publisher={ACM New York, NY}
}

@article{waltersdorfer2025leveraging,
  title={Leveraging Knowledge Graphs for AI System Auditing and Transparency},
  author={Waltersdorfer, Laura and Sabou, Marta},
  journal={Journal of Web Semantics},
  volume={84},
  pages={100849},
  year={2025},
  publisher={Elsevier}
}

@inproceedings{schmolliadversarially,
  title={Adversarially Robust Spiking Neural Networks with Sparse Connectivity},
  author={Schmolli, Mathias and Baronig, Maximilian and Legenstein, Robert and Ozdenizci, Ozan},
  booktitle={The Second Conference on Parsimony and Learning (Proceedings Track)}
}

@article{wu2025simple,
  title={A simple model for Behavioral Time Scale Synaptic Plasticity (BTSP) provides content addressable memory with binary synapses and one-shot learning},
  author={Wu, Yujie and Maass, Wolfgang},
  journal={Nature communications},
  volume={16},
  number={1},
  pages={342},
  year={2025},
  publisher={Nature Publishing Group UK London}
}

@inproceedings{poppel2025flashrnn,
  title={FlashRNN: I/O-aware optimization of traditional RNNs on modern hardware},
  author={P{\"o}ppel, Korbinian and Beck, Maximilian and Hochreiter, Sepp},
  booktitle={The Thirteenth International Conference on Learning Representations},
  year={2025}
}

@inproceedings{aichberger2025improving,
  title={Improving uncertainty estimation through semantically diverse language generation},
  author={Aichberger, Lukas and Schweighofer, Kajetan and Ielanskyi, Mykyta and Hochreiter, Sepp},
  booktitle={The Thirteenth International Conference on Learning Representations},
  year={2025}
}

@inproceedings{ozdenizciprivacy,
  title={Privacy-Aware Lifelong Learning},
  author={Ozdenizci, Ozan and Rueckert, Elmar and Legenstein, Robert},
  booktitle={The Thirteenth International Conference on Learning Representations}
}

@inproceedings{ganiantraining,
  title={Training One-Dimensional Graph Neural Networks is NP-Hard},
  author={Ganian, Robert and Rocton, Mathis and Wietheger, Simon},
  booktitle={The Thirteenth International Conference on Learning Representations}
}

@article{herwanto2025ontology,
  title={Ontology Corpora for LLM-based Knowledge Engineering Research},
  author={Herwanto, Guntur Budi and Tsaneva, Stefani and Sabou, Marta},
  year={2025}
}

@article{svoboda2024density,
  title={Density amplifiers of cooperation for spatial games},
  author={Svoboda, Jakub and Chatterjee, Krishnendu},
  journal={Proceedings of the National Academy of Sciences},
  volume={121},
  number={50},
  pages={e2405605121},
  year={2024},
  publisher={National Academy of Sciences}
}

@inproceedings{mihindukulasooriya2024scholarly,
  title={Scholarly Wikidata: Population and Exploration of Conference Data in Wikidata Using LLMs},
  author={Mihindukulasooriya, Nandana and Tiwari, Sanju and Dobriy, Daniil and Nielsen, Finn {\AA}rup and Chhetri, Tek Raj and Polleres, Axel},
  booktitle={International Conference on Knowledge Engineering and Knowledge Management},
  pages={243--259},
  year={2024},
  organization={Springer}
}

@inproceedings{tsaneva2024benchmarking,
  title={Benchmarking ontology validation capabilities of llms},
  author={Tsaneva, Stefani and Herwanto, Guntur Budi and Sabou, Marta},
  booktitle={The Semantic Web--ISWC},
  pages={11--15},
  year={2024}
}

@inproceedings{chew2024asp,
  title={ASP-QRAT: A Conditionally Optimal Dual Proof System for ASP},
  author={Chew, Leroy and de Colnet, Alexis and Szeider, Stefan},
  booktitle={Proceedings of the TwentyFirst International Conference on Principles of Knowledge Representation and Reasoning},
  pages={253--263},
  year={2024}
}

@article{schedl2024importance,
  title={The Importance of Cognitive Biases in the Recommendation Ecosystem: Evidence of Feature-Positive Effect, Ikea Effect, and Cultural Homophily},
  author={Schedl, Markus and Lesota, Oleg and Masoudian, Shahed},
  year={2024}
}

@inproceedings{faber2024solving,
  title={Solving Argumentation Problems Using Answer Set Programming with Quantifiers: Preliminary Report},
  author={Faber, Wolfgang},
  booktitle={Proceedings of the Workshop of 40th International Conference on Logic Programming (ICLP-WS 2024)(CEUR Workshop Proceedings, Vol. 3799)},
  year={2024}
}

@inproceedings{lesota2024oh,
  title={Oh, behave! country representation dynamics created by feedback loops in music recommender systems},
  author={Lesota, Oleg and Geiger, Jonas and Walder, Max and Kowald, Dominik and Schedl, Markus},
  booktitle={Proceedings of the 18th ACM Conference on Recommender Systems},
  pages={1022--1027},
  year={2024}
}

@inproceedings{ganhor2024multimodal,
  title={A Multimodal Single-Branch Embedding Network for Recommendation in Cold-Start and Missing Modality Scenarios},
  author={Ganh{\"o}r, Christian and Moscati, Marta and Hausberger, Anna and Nawaz, Shah and Schedl, Markus},
  booktitle={Proceedings of the 18th ACM Conference on Recommender Systems},
  pages={380--390},
  year={2024}
}

@article{chatterjee2024value,
  title={Value-Positivity for Matrix Games},
  author={Chatterjee, Krishnendu and Oliu-Barton, Miquel and Saona, Raimundo},
  journal={Mathematics of Operations Research},
  year={2024},
  publisher={INFORMS}
}

@article{innerebner2025hybrid,
  title={Hybrid Personalization Using Declarative and Procedural Memory Modules of the Cognitive Architecture ACT-R},
  author={Innerebner, Kevin and Kowald, Dominik and Schedl, Markus and Lex, Elisabeth},
  journal={arXiv preprint arXiv:2505.05083},
  year={2025}
}

@article{asadi2025qualitative,
  title={Qualitative Analysis of $$\backslash$omega $-Regular Objectives on Robust MDPs},
  author={Asadi, Ali and Chatterjee, Krishnendu and Goharshady, Ehsan Kafshdar and Karrabi, Mehrdad and Shafiee, Ali},
  journal={arXiv preprint arXiv:2505.04539},
  year={2025}
}

@article{moscati2025familiarizing,
  title={Familiarizing with Music: Discovery Patterns for Different Music Discovery Needs},
  author={Moscati, Marta and Afchar, Darius and Schedl, Markus and Sguerra, Bruno},
  journal={arXiv preprint arXiv:2505.03568},
  year={2025}
}

@article{baier2025multiplicative,
  title={Multiplicative Rewards in Markovian Models},
  author={Baier, Christel and Chatterjee, Krishnendu and Meggendorfer, Tobias and Piribauer, Jakob},
  journal={arXiv preprint arXiv:2504.18277},
  year={2025}
}

@article{yang2025surprising,
  title={A surprising link between cognitive maps, successor-relation based reinforcement learning, and BTSP},
  author={Yang, Yukun and Stoeckl, Christoph and Maass, Wolfgang},
  journal={bioRxiv},
  pages={2025--04},
  year={2025},
  publisher={Cold Spring Harbor Laboratory}
}

@article{chatterjee2025value,
  title={The Value Problem for Multiple-Environment MDPs with Parity Objective},
  author={Chatterjee, Krishnendu and Doyen, Laurent and Raskin, Jean-Fran{\c{c}}ois and Sankur, Ocan},
  journal={arXiv preprint arXiv:2504.15960},
  year={2025}
}

@article{zverev2025aside,
  title={ASIDE: Architectural Separation of Instructions and Data in Language Models},
  author={Zverev, Egor and Kortukov, Evgenii and Panfilov, Alexander and Tabesh, Soroush and Volkova, Alexandra and Lapuschkin, Sebastian and Samek, Wojciech and Lampert, Christoph H},
  journal={arXiv preprint arXiv:2503.10566},
  year={2025}
}

@article{kantz2025onset,
  title={OnSET: Ontology and Semantic Exploration Toolkit},
  author={Kantz, Benedikt and Innerebner, Kevin and Waldert, Peter and Lengauer, Stefan and Lex, Elisabeth and Schreck, Tobias},
  journal={arXiv preprint arXiv:2504.08373},
  year={2025}
}

@article{riccio2025imageset2text,
  title={ImageSet2Text: Describing Sets of Images through Text},
  author={Riccio, Piera and Galati, Francesco and Schweighofer, Kajetan and Garcia, Noa and Oliver, Nuria},
  journal={arXiv preprint arXiv:2503.19361},
  year={2025}
}

@article{yang2025parsimonious,
  title={A parsimonious model for learning order relations provides a principled explanation of diverse experimental data},
  author={Yang, Yukun and Maass, Wolfgang},
  journal={bioRxiv},
  pages={2025--03},
  year={2025},
  publisher={Cold Spring Harbor Laboratory}
}

@article{aichberger2025attacking,
  title={Attacking Multimodal OS Agents with Malicious Image Patches},
  author={Aichberger, Lukas and Paren, Alasdair and Gal, Yarin and Torr, Philip and Bibi, Adel},
  journal={arXiv preprint arXiv:2503.10809},
  year={2025}
}

@article{ganian2025computational,
  title={The Computational Complexity of Positive Non-Clashing Teaching in Graphs},
  author={Ganian, Robert and Khazaliya, Liana and Inerney, Fionn Mc and Rocton, Mathis},
  journal={arXiv preprint arXiv:2503.07665},
  year={2025}
}

@article{harviainen2025optimal,
  title={Optimal Decision Tree Pruning Revisited: Algorithms and Complexity},
  author={Harviainen, Juha and Sommer, Frank and Sorge, Manuel and Szeider, Stefan},
  journal={arXiv preprint arXiv:2503.03576},
  year={2025}
}

@article{eiben2025minor,
  title={A Minor-Testing Approach for Coordinated Motion Planning with Sliding Robots},
  author={Eiben, Eduard and Ganian, Robert and Kanj, Iyad and Sridharan, Ramanujan M},
  journal={arXiv preprint arXiv:2502.21175},
  year={2025}
}

@article{radler2025generative,
  title={Generative Topology Optimization: Exploring Diverse Solutions in Structural Design},
  author={Radler, Andreas and Volkmann, Eric and Brandstetter, Johannes and Berzins, Arturs},
  journal={arXiv preprint arXiv:2502.13174},
  year={2025}
}

@article{kalinin2025continual,
  title={Continual Release Moment Estimation with Differential Privacy},
  author={Kalinin, Nikita P and Upadhyay, Jalaj and Lampert, Christoph H},
  journal={arXiv preprint arXiv:2502.06597},
  year={2025}
}

@article{pourya2025dealing,
  title={DEALing with Image Reconstruction: Deep Attentive Least Squares},
  author={Pourya, Mehrsa and Kobler, Erich and Unser, Michael and Neumayer, Sebastian},
  journal={arXiv preprint arXiv:2502.04079},
  year={2025}
}

@misc{wu2025neuralcollapseunconstrainedfeatures,
      title={Neural Collapse Beyond the Unconstrained Features Model: Landscape, Dynamics, and Generalization in the Mean-Field Regime}, 
      author={Diyuan Wu and Marco Mondelli},
      year={2025},
      eprint={2501.19104},
      archivePrefix={arXiv},
      primaryClass={cs.LG},
      url={https://arxiv.org/abs/2501.19104}, 
}

@article{schidler2025extracting,
  title={Extracting Problem Structure with LLMs for Optimized SAT Local Search},
  author={Schidler, Andr{\'e} and Szeider, Stefan},
  journal={arXiv preprint arXiv:2501.14630},
  year={2025}
}

@article{voboril2024realtime,
  title={Realtime generation of streamliners with large language models},
  author={Voboril, Florentina and Ramaswamy, Vaidyanathan Peruvemba and Szeider, Stefan},
  journal={arXiv preprint arXiv:2408.10268},
  year={2024}
}

@article{kirchweger2025smart,
  title={Smart Cubing for Graph Search: A Comparative Study},
  author={Kirchweger, Markus and Xia, Hai and Peitl, Tom{\'a}{\v{s}} and Szeider, Stefan},
  journal={arXiv preprint arXiv:2501.17201},
  year={2025}
}

@article{chatterjee2025fixed,
  title={Fixed Point Certificates for Reachability and Expected Rewards in MDPs},
  author={Chatterjee, Krishnendu and Quatmann, Tim and Sch{\"a}ffeler, Maximilian and Weininger, Maximilian and Winkler, Tobias and Zilken, Daniel},
  journal={arXiv preprint arXiv:2501.11467},
  year={2025}
}

@article{ma2024efficient,
  title={Efficient MedSAMs: Segment Anything in Medical Images on Laptop},
  author={Ma, Jun and Li, Feifei and Kim, Sumin and Asakereh, Reza and Le, Bao-Hiep and Nguyen-Vu, Dang-Khoa and Pfefferle, Alexander and Wei, Muxin and Gao, Ruochen and Lyu, Donghang and others},
  journal={arXiv preprint arXiv:2412.16085},
  year={2024}
}

@article{hofstadler2024symmetries,
  title={Symmetries of Dependency Quantified Boolean Formulas},
  author={Hofstadler, Clemens and Kauers, Manuel and Seidl, Martina},
  journal={arXiv preprint arXiv:2410.15848},
  year={2024}
}

@misc{asadi2024concurrentstochasticgamesstatefuldiscounted,
      title={Concurrent Stochastic Games with Stateful-discounted and Parity Objectives: Complexity and Algorithms}, 
      author={Ali Asadi and Krishnendu Chatterjee and Raimundo Saona and Jakub Svoboda},
      year={2024},
      eprint={2405.02486},
      archivePrefix={arXiv},
      primaryClass={cs.GT},
      url={https://arxiv.org/abs/2405.02486}, 
}

@article{frohmann2024segment,
  title={Segment Any Text: A universal approach for robust, efficient and adaptable sentence segmentation},
  author={Frohmann, Markus and Sterner, Igor and Vuli{\'c}, Ivan and Minixhofer, Benjamin and Schedl, Markus},
  journal={arXiv preprint arXiv:2406.16678},
  year={2024}
}

@article{masoudian2024unlabeled,
  title={Unlabeled Debiasing in Downstream Tasks via Class-wise Low Variance Regularization},
  author={Masoudian, Shahed and Frohmann, Markus and Rekabsaz, Navid and Schedl, Markus},
  journal={arXiv preprint arXiv:2409.19541},
  year={2024}
}

@article{ordyniak2024explaining,
  title={Explaining Decisions in ML Models: a Parameterized Complexity Analysis},
  author={Ordyniak, Sebastian and Paesani, Giacomo and Rychlicki, Mateusz and Szeider, Stefan},
  journal={arXiv preprint arXiv:2407.15780},
  year={2024}
}