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Li C., Brockmoeller T., Jegelka S., Gupta S., Karalias N. (2026). On the Fragility of Tasks under Composition: Implicit Budgeting in Language Models. arXiv preprint. (arxiv, pdf, project, code, discussion, bibtex)
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Karalias N., Peltonen S., Kostin N., Jegelka S. (2026). On the fundamentals of gradient-based SAT solvers. NeurIPS 2026, Sydney (to appear). (pdf, blogpost)
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Karalias N., Rafiey A., Xu Y., Luo Z., Tahmasebi B., Jiang C., Jegelka S. (2025) Geometric Algorithms for Neural Combinatorial Optimization with Constraints. NeurIPS 2025. (arxiv, proceedings, code, blogpost, bibtex)
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Yau M., Karalias N., Lu E., Xu J., Jegelka S. (2024). Are Graph Neural Networks Optimal Approximation Algorithms? NeurIPS 2024 Spotlight. (arxiv, proceedings, code, blogpost, MIT-CSAIL news coverage, video presentation)
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Karalias N., Robinson J., Loukas A., Jegelka S. (2022). Neural Set Function Extensions: Learning with Discrete Functions in High Dimensions. NeurIPS 2022. (arxiv, proceedings, code, blogpost, video presentation)
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Karalias N., & Loukas A. (2020). Erdos Goes Neural: an Unsupervised Learning Framework for Combinatorial Optimization on Graphs. NeurIPS 2020 Oral (arxiv, code, blogpost, coauthor’s blogpost, short presentation, podcast, bibtex)
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Bouritsas G., Loukas A., Karalias N., Bronstein M. (2021). Partition and Code: learning how to compress graphs. NeurIPS 2021. (arxiv, code, slides, bibtex)
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Efthymiou N., Karalias N. (2025). Probabilistic Loss Functions for Self-Supervised SAT Solvers. NeurIPS 2025 DiffCOAlg Workshop.
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Kostin N., Karalias N., Jegelka S. (2025). On the benefits of label preserving augmentations for self-supervised SAT solvers. NeurIPS 2025 DiffCOAlg Workshop. (pdf)
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He Y., Nerem R. R., Stoll T., Cantürk S., Georgiev D., Wittig S., Qian C., Geerts F., Jegelka S., Vitercik E., Wang Y., Karalias N., Morris C. (2026). Neural Algorithmic Reasoning Must Explain When Neuralization Adds Value. ICML 2026 AI4Math Workshop (Poster). (paper)
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Rafiey A., Xu Y., Karalias N. (2026). Self-Supervised Combinatorial Optimization with Constraints via Frank–Wolfe. OPT 2026 Workshop (Poster). (arxiv)