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12340 Publications


2024 | Published | Journal Article | IST-REx-ID: 15165 | OA
B. B. GOSWAMI, “A pre-monsoon signal of false alarms of Indian monsoon droughts,” Geophysical Research Letters, vol. 51, no. 5. Wiley, 2024.
[Published Version] View | Files available | DOI | WoS
 

2024 | Published | Journal Article | IST-REx-ID: 18708 | OA
S. Paik et al., “Exploring causes of distinct regional and subseasonal Indian summer monsoon precipitation responses to CO2 removal,” npj Climate and Atmospheric Science, vol. 7. Springer Nature, 2024.
[Published Version] View | Files available | DOI | WoS
 

2024 | Research Data Reference | IST-REx-ID: 19307 | OA
Y.-L. Hwong and C. J. Muller, “Data - The unreasonable efficiency of total rain evaporation removal in triggering convective self-aggregation.” Zenodo, 2024.
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2024 | Published | Journal Article | IST-REx-ID: 17203 | OA
C. Hafner, M. Ly, and C. Wojtan, “Spin-it faster: Quadrics solve all topology optimization problems that depend only on mass moments,” Transactions on Graphics, vol. 43, no. 4. Association for Computing Machinery, 2024.
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2024 | Published | Journal Article | IST-REx-ID: 15313 | OA
L. N. Agasthya and C. J. Muller, “Dynamics and scaling of internally cooled convection,” Communications in Nonlinear Science and Numerical Simulation, vol. 134. Elsevier, 2024.
[Published Version] View | Files available | DOI | WoS | arXiv
 

2024 | Published | Conference Paper | IST-REx-ID: 17411 | OA
J. A. Scott, H. Zakerinia, and C. Lampert, “PEFLL: Personalized federated learning by learning to learn,” in 12th International Conference on Learning Representations, Vienna, Austria, 2024.
[Published Version] View | Files available | arXiv
 

2024 | Published | Conference Paper | IST-REx-ID: 18120 | OA
J. A. Scott and Á. Cahill, “Improved modelling of federated datasets using mixtures-of-Dirichlet-multinomials,” in Proceedings of the 41st International Conference on Machine Learning, Vienna, Austria, 2024, vol. 235, pp. 44012–44037.
[Preprint] View | Files available | Download Preprint (ext.) | arXiv
 

2024 | Published | Conference Paper | IST-REx-ID: 18118 | OA
H. Zakerinia, A. Behjati, and C. Lampert, “More flexible PAC-Bayesian meta-learning by learning learning algorithms,” in Proceedings of the 41st International Conference on Machine Learning, Vienna, Austria, 2024, vol. 235, pp. 58122–58139.
[Published Version] View | Download Published Version (ext.) | arXiv
 

2024 | Published | Conference Paper | IST-REx-ID: 17332 | OA
I. Kokorin, V. Yudov, V. Aksenov, and D.-A. Alistarh, “Wait-free trees with asymptotically-efficient range queries,” in 2024 IEEE International Parallel and Distributed Processing Symposium, San Francisco, CA, United States, 2024, pp. 169–179.
[Preprint] View | DOI | Download Preprint (ext.) | WoS | arXiv
 

2024 | Published | Conference Paper | IST-REx-ID: 15011 | OA
E. Kurtic, T. Hoefler, and D.-A. Alistarh, “How to prune your language model: Recovering accuracy on the ‘Sparsity May Cry’ benchmark,” in Proceedings of Machine Learning Research, Hongkong, China, 2024, vol. 234, pp. 542–553.
[Preprint] View | Download Preprint (ext.) | arXiv
 

2024 | Published | Conference Paper | IST-REx-ID: 18070
B. Chatterjee, V. Kungurtsev, and D.-A. Alistarh, “Federated SGD with local asynchrony,” in Proceedings of the 44th International Conference on Distributed Computing Systems, Jersey City, NJ, United States, 2024, pp. 857–868.
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2024 | Published | Conference Paper | IST-REx-ID: 17329 | OA
D.-A. Alistarh, K. Chatterjee, M. Karrabi, and J. M. Lazarsfeld, “Game dynamics and equilibrium computation in the population protocol model,” in Proceedings of the 43rd Annual ACM Symposium on Principles of Distributed Computing, Nantes, France, 2024, pp. 40–49.
[Published Version] View | Files available | DOI
 

2024 | Published | Conference Paper | IST-REx-ID: 18121 | OA
A. S. Moakhar, E. B. Iofinova, E. Frantar, and D.-A. Alistarh, “SPADE: Sparsity-guided debugging for deep neural networks,” in Proceedings of the 41st International Conference on Machine Learning, Vienna, Austria, 2024, vol. 235, pp. 45955–45987.
[Preprint] View | Files available | Download Preprint (ext.) | arXiv
 

2024 | Published | Conference Paper | IST-REx-ID: 18976 | OA
R. Islamov, M. Safaryan, and D.-A. Alistarh, “AsGrad: A sharp unified analysis of asynchronous-SGD algorithms,” in Proceedings of The 27th International Conference on Artificial Intelligence and Statistics, Valencia, Spain, 2024, vol. 238, pp. 649–657.
[Preprint] View | Download Preprint (ext.) | arXiv
 

2024 | Published | Conference Paper | IST-REx-ID: 18113 | OA
V. Egiazarian, A. Panferov, D. Kuznedelev, E. Frantar, A. Babenko, and D.-A. Alistarh, “Extreme compression of large language models via additive quantization,” in Proceedings of the 41st International Conference on Machine Learning, Vienna, Austria, 2024, vol. 235, pp. 12284–12303.
[Preprint] View | Download Preprint (ext.) | arXiv
 

2024 | Published | Conference Paper | IST-REx-ID: 18975 | OA
I.-V. Modoranu, A. Kalinov, E. Kurtic, E. Frantar, and D.-A. Alistarh, “Error feedback can accurately compress preconditioners,” in 41st International Conference on Machine Learning, Vienna, Austria, 2024, vol. 235, pp. 35910–35933.
[Preprint] View | Download Preprint (ext.) | arXiv
 

2024 | Published | Conference Paper | IST-REx-ID: 18977 | OA
T. Dettmers et al., “SpQR: A sparse-quantized representation for near-lossless LLM weight compression,” in 12th International Conference on Learning Representations, Vienna, Austria, 2024.
[Preprint] View | Download Preprint (ext.) | arXiv
 

2024 | Published | Conference Paper | IST-REx-ID: 17093 | OA
H. Zakerinia, S. Talaei, G. Nadiradze, and D.-A. Alistarh, “Communication-efficient federated learning with data and client heterogeneity,” in Proceedings of the 27th International Conference on Artificial Intelligence and Statistics, Valencia, Spain, 2024, vol. 238, pp. 3448–3456.
[Preprint] View | Download Preprint (ext.) | arXiv
 

2024 | Published | Conference Paper | IST-REx-ID: 18117 | OA
M. Nikdan, S. Tabesh, E. Crncevic, and D.-A. Alistarh, “RoSA: Accurate parameter-efficient fine-tuning via robust adaptation,” in Proceedings of the 41st International Conference on Machine Learning, Vienna, Austria, 2024, vol. 235, pp. 38187–38206.
[Preprint] View | Files available | Download Preprint (ext.) | arXiv
 

2024 | Published | Journal Article | IST-REx-ID: 15315 | OA
D. Brückner and C. P. Broedersz, “Learning dynamical models of single and collective cell migration: a review,” Reports on Progress in Physics, vol. 87, no. 5. IOP Publishing, 2024.
[Published Version] View | Files available | DOI | WoS | PubMed | Europe PMC | arXiv
 

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