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25 Publications
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.” 41st International Conference on Machine Learning, vol. 235, ML Research Press, 2024, pp. 4300–28.
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2024 | Published | Conference Paper | IST-REx-ID: 18975 |

Modoranu, Ionut-Vlad, et al. “Error Feedback Can Accurately Compress Preconditioners.” 41st International Conference on Machine Learning, vol. 235, ML Research Press, 2024, pp. 35910–33.
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| arXiv
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.” 41st International Conference on Machine Learning, vol. 235, ML Research Press, 2024, pp. 4267–99.
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| arXiv
2024 | Published | Conference Paper | IST-REx-ID: 18971 |

Arefin, Rifat, et al. “Unsupervised Concept Discovery Mitigates Spurious Correlations.” Proceedings of the 41st International Conference on Machine Learning, vol. 235, ML Research Press, 2024, pp. 1672–88.
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| arXiv
2024 | Published | Conference Paper | IST-REx-ID: 18976 |

Islamov, Rustem, et al. “AsGrad: A Sharp Unified Analysis of Asynchronous-SGD Algorithms.” Proceedings of The 27th International Conference on Artificial Intelligence and Statistics, vol. 238, ML Research Press, 2024, pp. 649–57.
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| arXiv
2024 | Published | Conference Paper | IST-REx-ID: 17093 |

Zakerinia, Hossein, et al. “Communication-Efficient Federated Learning with Data and Client Heterogeneity.” Proceedings of the 27th International Conference on Artificial Intelligence and Statistics, vol. 238, ML Research Press, 2024, pp. 3448–56.
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| arXiv
2024 | Published | Conference Paper | IST-REx-ID: 15011 |

Kurtic, Eldar, et al. “How to Prune Your Language Model: Recovering Accuracy on the ‘Sparsity May Cry’ Benchmark.” Proceedings of Machine Learning Research, vol. 234, ML Research Press, 2024, pp. 542–53.
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| arXiv
2024 | Published | Conference Paper | IST-REx-ID: 18113 |

Egiazarian, Vage, et al. “Extreme Compression of Large Language Models via Additive Quantization.” Proceedings of the 41st International Conference on Machine Learning, vol. 235, ML Research Press, 2024, pp. 12284–303.
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2024 | Published | Conference Paper | IST-REx-ID: 18114 |

Pervez, Adeel A., et al. “Mechanistic Neural Networks for Scientific Machine Learning.” Proceedings of the 41st International Conference on Machine Learning, vol. 235, ML Research Press, 2024, pp. 40484–501.
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| arXiv
2024 | Published | Conference Paper | IST-REx-ID: 18115 |

Axiotis, Kyriakos, et al. “Data-Efficient Learning via Clustering-Based Sensitivity Sampling: Foundation Models and Beyond.” Proceedings of the 41st International Conference on Machine Learning, vol. 235, ML Research Press, 2024, pp. 2086–107.
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| arXiv
2024 | Published | Conference Paper | IST-REx-ID: 18116 |

La Tour, Max Dupré, et al. “Making Old Things New: A Unified Algorithm for Differentially Private Clustering.” Proceedings of the 41st International Conference on Machine Learning, vol. 235, ML Research Press, 2024, pp. 12046–86.
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| arXiv
2024 | Published | Conference Paper | IST-REx-ID: 18117 |

Nikdan, Mahdi, et al. “RoSA: Accurate Parameter-Efficient Fine-Tuning via Robust Adaptation.” Proceedings of the 41st International Conference on Machine Learning, vol. 235, ML Research Press, 2024, pp. 38187–206.
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| arXiv
2024 | Published | Conference Paper | IST-REx-ID: 18118 |

Zakerinia, Hossein, et al. “More Flexible PAC-Bayesian Meta-Learning by Learning Learning Algorithms.” Proceedings of the 41st International Conference on Machine Learning, vol. 235, ML Research Press, 2024, pp. 58122–39.
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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.” Proceedings of the 41st International Conference on Machine Learning, vol. 235, ML Research Press, 2024, pp. 44012–37.
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2024 | Published | Conference Paper | IST-REx-ID: 18121 |

Moakhar, Arshia Soltani, et al. “SPADE: Sparsity-Guided Debugging for Deep Neural Networks.” Proceedings of the 41st International Conference on Machine Learning, vol. 235, ML Research Press, 2024, pp. 45955–87.
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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.” Proceedings of the 40th International Conference on Machine Learning, vol. 202, ML Research Press, 2023, pp. 10323–37.
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| arXiv
2023 | Published | Conference Paper | IST-REx-ID: 14460 |

Nikdan, Mahdi, et al. “SparseProp: Efficient Sparse Backpropagation for Faster Training of Neural Networks at the Edge.” Proceedings of the 40th International Conference on Machine Learning, vol. 202, ML Research Press, 2023, pp. 26215–27.
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| arXiv
2023 | Published | Conference Paper | IST-REx-ID: 14461 |

Markov, Ilia, et al. “Quantized Distributed Training of Large Models with Convergence Guarantees.” Proceedings of the 40th International Conference on Machine Learning, vol. 202, ML Research Press, 2023, pp. 24020–44.
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| arXiv
2023 | Published | Conference Paper | IST-REx-ID: 14462 |

Fichtenberger, Hendrik, et al. “Constant Matters: Fine-Grained Error Bound on Differentially Private Continual Observation.” Proceedings of the 40th International Conference on Machine Learning, vol. 202, ML Research Press, 2023, pp. 10072–92.
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2023 | Published | Conference Paper | IST-REx-ID: 14459 |

Shevchenko, Alexander, et al. “Fundamental Limits of Two-Layer Autoencoders, and Achieving Them with Gradient Methods.” Proceedings of the 40th International Conference on Machine Learning, vol. 202, ML Research Press, 2023, pp. 31151–209.
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| arXiv