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6293 Publications

2024 | Published | Conference Paper | IST-REx-ID: 18971 | OA
Arefin R, Zhang Y, Baratin A, et al. Unsupervised concept discovery mitigates spurious correlations. In: Proceedings of the 41st International Conference on Machine Learning. Vol 235. ML Research Press; 2024:1672-1688.
[Preprint] View | Files available | Download Preprint (ext.) | arXiv
 
2024 | Published | Conference Paper | IST-REx-ID: 18972 | OA
Bombari S, Mondelli M. How spurious features are memorized: Precise analysis for random and NTK features. In: 41st International Conference on Machine Learning. Vol 235. ML Research Press; 2024:4267-4299.
[Preprint] View | Download Preprint (ext.) | arXiv
 
2024 | Published | Conference Paper | IST-REx-ID: 18973 | OA
Bombari S, Mondelli M. Towards understanding the word sensitivity of attention layers: A study via random features. In: 41st International Conference on Machine Learning. Vol 235. ML Research Press; 2024:4300-4328.
[Preprint] View | Download Preprint (ext.) | arXiv
 
2024 | Published | Conference Paper | IST-REx-ID: 18974 | OA
Svoboda J, Bansal S, Chatterjee K. Reinforcement learning from reachability specifications: PAC guarantees with expected conditional distance. In: 41st International Conference on Machine Learning. Vol 235. ML Research Press; 2024:47331-47344.
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2024 | Published | Conference Paper | IST-REx-ID: 18975 | OA
Modoranu I-V, Kalinov A, Kurtic E, Frantar E, Alistarh D-A. Error feedback can accurately compress preconditioners. In: 41st International Conference on Machine Learning. Vol 235. ML Research Press; 2024:35910-35933.
[Preprint] View | Download Preprint (ext.) | arXiv
 
2024 | Published | Conference Paper | IST-REx-ID: 18976 | OA
Islamov R, Safaryan M, Alistarh D-A. AsGrad: A sharp unified analysis of asynchronous-SGD algorithms. In: Proceedings of The 27th International Conference on Artificial Intelligence and Statistics. Vol 238. ML Research Press; 2024:649-657.
[Preprint] View | Download Preprint (ext.) | arXiv
 
2024 | Published | Conference Paper | IST-REx-ID: 18977 | OA
Dettmers T, Svirschevski RA, Egiazarian V, et al. SpQR: A sparse-quantized representation for near-lossless LLM weight compression. In: 12th International Conference on Learning Representations. OpenReview; 2024.
[Preprint] View | Download Preprint (ext.) | arXiv
 
2024 | Draft | Preprint | IST-REx-ID: 18981 | OA
Brown A, Draganov O. Discrete microlocal Morse theory. arXiv. doi:10.48550/arXiv.2209.14993
[Preprint] View | Files available | DOI | Download Preprint (ext.) | arXiv
 
2024 | Published | Conference Paper | IST-REx-ID: 18996 | OA
Chen T, Bello K, Locatello F, Aragam B, Ravikumar PK. Identifying general mechanism shifts in linear causal representations. In: 38th Conference on Neural Information Processing Systems. Vol 37. Neural Information Processing Systems Foundation; 2024.
[Published Version] View | Files available | arXiv
 
2024 | Published | Conference Paper | IST-REx-ID: 18998 | OA
Draganov O, Skiena S. The shape of word embeddings: Quantifying non-isometry with topological data analysis. In: Findings of the Association for Computational Linguistics: EMNLP 2024. Association for Computational Linguistics; 2024:12080-12099. doi:10.18653/v1/2024.findings-emnlp.705
[Published Version] View | Files available | DOI | arXiv
 
2024 | Submitted | Preprint | IST-REx-ID: 18999 | OA
Cultrera di Montesano S, Draganov O, Edelsbrunner H, Saghafian M. Chromatic topological data analysis. arXiv. doi:10.48550/ARXIV.2406.04102
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
2024 | Published | Conference Paper | IST-REx-ID: 19005 | OA
Yao D, Muller CJ, Locatello F. Marrying causal representation learning with dynamical systems for science. In: 38th Conference on Neural Information Processing Systems. Vol 37. Neural Information Processing Systems Foundation; 2024.
[Published Version] View | Files available | arXiv
 
2024 | Published | Conference Paper | IST-REx-ID: 19007 | OA
Kori A, Locatello F, Santhirasekaram A, Toni F, Glocker B, De Sousa Ribeiro F. Identifiable object-centric representation learning via probabilistic slot attention. In: 38th Conference on Neural Information Processing Systems. Vol 37. Neural Information Processing Systems Foundation; 2024.
[Published Version] View | Files available | arXiv
 
2024 | Draft | Preprint | IST-REx-ID: 19013 | OA
Glas J, Hase-Liu M. Terminal singularities of the moduli space of curves on low degree hypersurfaces and the circle method. arXiv. doi:10.48550/arXiv.2412.14923
[Preprint] View | Files available | DOI | Download Preprint (ext.) | arXiv
 
2024 | Published | Journal Article | IST-REx-ID: 19051 | OA
Browning TD. The polynomial sieve and equal sums of like polynomials. International Mathematics Research Notices. 2024;2024(13):10165-10168. doi:10.1093/imrn/rnae066
[Published Version] View | Files available | DOI | WoS
 
2024 | Published | Preprint | IST-REx-ID: 19063 | OA
Zverev E, Abdelnabi S, Tabesh S, Fritz M, Lampert C. Can LLMs separate instructions from data? And what do we even mean by that? arXiv. 2024. doi:10.48550/arXiv.2403.06833
[Preprint] View | Files available | DOI | Download Preprint (ext.) | arXiv
 
2024 | Research Data Reference | IST-REx-ID: 19307 | OA
Hwong Y-L, Muller CJ. Data - The unreasonable efficiency of total rain evaporation removal in triggering convective self-aggregation. 2024. doi:10.5281/ZENODO.10687169
[Published Version] View | Files available | DOI | Download Published Version (ext.)
 
2024 | Published | Journal Article | IST-REx-ID: 19408 | OA
Verwimp E, Aljundi R, Ben-David S, et al. Continual learning: Applications and the road forward. Transactions on Machine Learning Research. 2024;2024.
[Published Version] View | Files available | arXiv
 
2024 | Draft | Preprint | IST-REx-ID: 19425 | OA
Karle V, Lemeshko M, Bouhon A, Slager R-J, Ünal FN. Anomalous multi-gap topological phases in periodically driven quantum  rotors. arXiv. doi:10.48550/arXiv.2408.16848
[Preprint] View | Files available | DOI | Download Preprint (ext.) | arXiv
 
2024 | Published | Journal Article | IST-REx-ID: 19446
Nees F, Renner P, Holz NE, Polemiti E, Siehl S, Hese S, Schepanski K, Schumann G, Walter H, Heinz A, Ralser M, Twardziok S, Vaidya N, Bernas A, Serin E, Jentsch M, Hitchen E, Kebir H, Lett TA, Roy JC, Eils R, Taron UH, Schütz T, Banks J, Banaschewski T, Jansone K, Christmann N, Meyer-Lindenberg A, Tost H, Holz N, Schwarz E, Stringaris A, Neidhart M, Seefried B, Aden R, Andreassen OA, Westlye LT, Van Der Meer D, Fernandez S, Kjelkenes R, Ask H, Rapp M, Tschorn M, Böttger SJ, Marquand A, Novarino G, Marr L, Slater M, Viapiana GF, Orosa FE, Gallego J, Pastor A, Forstner AJ, Hoffmann P, Nöthen MM, Claus I, Miller A, Mathey CM, Heilmann-Heimbach S, Sommer P, Patraskaki M, Wilbertz J, Schmitt K, Jirsa V, Petkoski S, Pitel S, Otten L, Athanasiadis AP, Pearmund C, Spanlang B, Alvarez E, Sanchez M, Giner A, Jia T, Gong Y, Xia Y, Chang X, Calhoun V, Liu J, Schwalber A, Thompson P, Clinton N, Desrivières S, Young AH, Stahl B, Ogoh G. 2024. Large-scale population data enrichment in mental health research. Nature Mental Health. 2(10), 1124–1127.
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