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25 Publications
2024 | Published | Conference Paper | IST-REx-ID: 17093 |

Zakerinia, Hossein, Shayan Talaei, Giorgi Nadiradze, and Dan-Adrian Alistarh. “Communication-Efficient Federated Learning with Data and Client Heterogeneity.” In Proceedings of the 27th International Conference on Artificial Intelligence and Statistics, 238:3448–56. ML Research Press, 2024.
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2024 | Published | Conference Paper | IST-REx-ID: 15011 |

Kurtic, Eldar, Torsten Hoefler, and Dan-Adrian Alistarh. “How to Prune Your Language Model: Recovering Accuracy on the ‘Sparsity May Cry’ Benchmark.” In Proceedings of Machine Learning Research, 234:542–53. ML Research Press, 2024.
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2024 | Published | Conference Paper | IST-REx-ID: 18113 |

Egiazarian, Vage, Andrei Panferov, Denis Kuznedelev, Elias Frantar, Artem Babenko, and Dan-Adrian Alistarh. “Extreme Compression of Large Language Models via Additive Quantization.” In Proceedings of the 41st International Conference on Machine Learning, 235:12284–303. ML Research Press, 2024.
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| arXiv
2024 | Published | Conference Paper | IST-REx-ID: 18114 |

Pervez, Adeel A, Francesco Locatello, and Efstratios Gavves. “Mechanistic Neural Networks for Scientific Machine Learning.” In Proceedings of the 41st International Conference on Machine Learning, 235:40484–501. ML Research Press, 2024.
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| arXiv
2024 | Published | Conference Paper | IST-REx-ID: 18117 |

Nikdan, Mahdi, Soroush Tabesh, Elvir Crncevic, and Dan-Adrian Alistarh. “RoSA: Accurate Parameter-Efficient Fine-Tuning via Robust Adaptation.” In Proceedings of the 41st International Conference on Machine Learning, 235:38187–206. ML Research Press, 2024.
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| arXiv
2024 | Published | Conference Paper | IST-REx-ID: 18118 |

Zakerinia, Hossein, Amin Behjati, and Christoph Lampert. “More Flexible PAC-Bayesian Meta-Learning by Learning Learning Algorithms.” In Proceedings of the 41st International Conference on Machine Learning, 235:58122–39. ML Research Press, 2024.
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| arXiv
2024 | Published | Conference Paper | IST-REx-ID: 18120 |

Scott, Jonathan A, and Áine Cahill. “Improved Modelling of Federated Datasets Using Mixtures-of-Dirichlet-Multinomials.” In Proceedings of the 41st International Conference on Machine Learning, 235:44012–37. ML Research Press, 2024.
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| arXiv
2024 | Published | Conference Paper | IST-REx-ID: 18975 |

Modoranu, Ionut-Vlad, Aleksei Kalinov, Eldar Kurtic, Elias Frantar, and Dan-Adrian Alistarh. “Error Feedback Can Accurately Compress Preconditioners.” In 41st International Conference on Machine Learning, 235:35910–33. ML Research Press, 2024.
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| arXiv
2024 | Published | Conference Paper | IST-REx-ID: 18971 |

Arefin, Rifat, Yan Zhang, Aristide Baratin, Francesco Locatello, Irina Rish, Dianbo Liu, and Kenji Kawaguchi. “Unsupervised Concept Discovery Mitigates Spurious Correlations.” In Proceedings of the 41st International Conference on Machine Learning, 235:1672–88. ML Research Press, 2024.
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| arXiv
2024 | Published | Conference Paper | IST-REx-ID: 18976 |

Islamov, Rustem, Mher Safaryan, and Dan-Adrian Alistarh. “AsGrad: A Sharp Unified Analysis of Asynchronous-SGD Algorithms.” In Proceedings of The 27th International Conference on Artificial Intelligence and Statistics, 238:649–57. ML Research Press, 2024.
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2024 | Published | Conference Paper | IST-REx-ID: 18973 |

Bombari, Simone, and Marco Mondelli. “Towards Understanding the Word Sensitivity of Attention Layers: A Study via Random Features.” In 41st International Conference on Machine Learning, 235:4300–4328. ML Research Press, 2024.
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2024 | Published | Conference Paper | IST-REx-ID: 18972 |

Bombari, Simone, and Marco Mondelli. “How Spurious Features Are Memorized: Precise Analysis for Random and NTK Features.” In 41st International Conference on Machine Learning, 235:4267–99. ML Research Press, 2024.
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| arXiv
2024 | Published | Conference Paper | IST-REx-ID: 18115 |

Axiotis, Kyriakos, Vincent Cohen-Addad, Monika Henzinger, Sammy Jerome, Vahab Mirrokni, David Saulpic, David P. Woodruff, and Michael Wunder. “Data-Efficient Learning via Clustering-Based Sensitivity Sampling: Foundation Models and Beyond.” In Proceedings of the 41st International Conference on Machine Learning, 235:2086–2107. ML Research Press, 2024.
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| arXiv
2024 | Published | Conference Paper | IST-REx-ID: 18116 |

La Tour, Max Dupré, Monika Henzinger, and David Saulpic. “Making Old Things New: A Unified Algorithm for Differentially Private Clustering.” In Proceedings of the 41st International Conference on Machine Learning, 235:12046–86. ML Research Press, 2024.
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| arXiv
2024 | Published | Conference Paper | IST-REx-ID: 18121 |

Moakhar, Arshia Soltani, Eugenia B Iofinova, Elias Frantar, and Dan-Adrian Alistarh. “SPADE: Sparsity-Guided Debugging for Deep Neural Networks.” In Proceedings of the 41st International Conference on Machine Learning, 235:45955–87. ML Research Press, 2024.
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| arXiv
2023 | Published | Conference Paper | IST-REx-ID: 14460 |

Nikdan, Mahdi, Tommaso Pegolotti, Eugenia B Iofinova, Eldar Kurtic, and Dan-Adrian Alistarh. “SparseProp: Efficient Sparse Backpropagation for Faster Training of Neural Networks at the Edge.” In Proceedings of the 40th International Conference on Machine Learning, 202:26215–27. ML Research Press, 2023.
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| arXiv
2023 | Published | Conference Paper | IST-REx-ID: 14458 |

Frantar, Elias, and Dan-Adrian Alistarh. “SparseGPT: Massive Language Models Can Be Accurately Pruned in One-Shot.” In Proceedings of the 40th International Conference on Machine Learning, 202:10323–37. ML Research Press, 2023.
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| arXiv
2023 | Published | Conference Paper | IST-REx-ID: 14461 |

Markov, Ilia, Adrian Vladu, Qi Guo, and Dan-Adrian Alistarh. “Quantized Distributed Training of Large Models with Convergence Guarantees.” In Proceedings of the 40th International Conference on Machine Learning, 202:24020–44. ML Research Press, 2023.
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| arXiv
2023 | Published | Conference Paper | IST-REx-ID: 14462 |

Fichtenberger, Hendrik, Monika Henzinger, and Jalaj Upadhyay. “Constant Matters: Fine-Grained Error Bound on Differentially Private Continual Observation.” In Proceedings of the 40th International Conference on Machine Learning, 202:10072–92. ML Research Press, 2023.
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| arXiv
2023 | Published | Conference Paper | IST-REx-ID: 14459 |

Shevchenko, Alexander, Kevin Kögler, Hamed Hassani, and Marco Mondelli. “Fundamental Limits of Two-Layer Autoencoders, and Achieving Them with Gradient Methods.” In Proceedings of the 40th International Conference on Machine Learning, 202:31151–209. ML Research Press, 2023.
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| arXiv