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22 Publications
2024 | Published | Conference Paper | IST-REx-ID: 19518 | 
D. Wu, I.-V. Modoranu, M. Safaryan, D. Kuznedelev, and D.-A. Alistarh, “The iterative optimal brain surgeon: Faster sparse recovery by leveraging second-order information,” in 38th Conference on Neural Information Processing Systems, Vancouver, Canada, 2024, vol. 37.
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| arXiv
2024 | Published | Conference Paper | IST-REx-ID: 19512 |
J. D. Andersson, M. Henzinger, R. Pagh, T. A. Steiner, and J. Upadhyay, “Continual counting with gradual privacy expiration,” in 38th Conference on Neural Information Processing Systems, Vancouver, Canada, 2024, vol. 37.
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| arXiv
2024 | Published | Conference Paper | IST-REx-ID: 19515 |
M. Fumero, M. Pegoraro, V. Maiorca, F. Locatello, and E. Rodolà, “Latent functional maps: A spectral framework for representation alignment,” in 38th Conference on Neural Information Processing Systems, Vancouver, Canada, 2024, vol. 37.
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| arXiv
2024 | Published | Conference Paper | IST-REx-ID: 19510 |
I.-V. Modoranu et al., “MICROADAM: Accurate adaptive optimization with low space overhead and provable convergence,” in 38th Conference on Neural Information Processing Systems, 2024, vol. 37.
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| arXiv
2024 | Published | Conference Paper | IST-REx-ID: 19517 |
D. Crisostomi, M. Fumero, D. Baieri, F. Bernard, and E. Rodolà, “C2M3: Cycle-consistent multi-model merging,” in 38th Conference on Neural Information Processing Systems, Vancouver, Canada, 2024, vol. 37.
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| arXiv
2024 | Published | Conference Paper | IST-REx-ID: 19511 |
S. Ashkboos et al., “QuaRot: Outlier-free 4-bit inference in rotated LLMs,” in 38th Conference on Neural Information Processing Systems, Vancouver, Canada, 2024, vol. 37.
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| arXiv
2024 | Published | Conference Paper | IST-REx-ID: 19519 |
V. Malinovskii et al., “PV-tuning: Beyond straight-through estimation for extreme LLM compression,” in 38th Conference on Neural Information Processing Systems, Vancouver, Canada, 2024, vol. 37.
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| arXiv
2023 | Published | Conference Paper | IST-REx-ID: 15363 |
M. Safaryan, A. Krumes, and D.-A. Alistarh, “Knowledge distillation performs partial variance reduction,” in 36th Conference on Neural Information Processing Systems, New Orleans, LA, United States, 2023, vol. 36.
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| arXiv
2023 | Published | Conference Paper | IST-REx-ID: 15364 |
M. Charikar, L. Hu, M. Henzinger, M. Vötsch, and E. Waingarten, “Simple, scalable and effective clustering via one-dimensional projections,” in 37th Conference on Neural Information Processing Systems, New Orleans, LA, United States, 2023, vol. 36.
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| arXiv
2022 | Published | Conference Paper | IST-REx-ID: 18876 |
P. Kocsis, P. Súkeník, G. Brasó, M. Niessner, L. Leal-Taixé, and I. Elezi, “The unreasonable effectiveness of fully-connected layers for low-data regimes,” in 36th Conference on Neural Information Processing Systems, New Orleans, LA, United States, 2022, vol. 35, pp. 1896–1908.
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| arXiv
2021 | Published | Conference Paper | IST-REx-ID: 11453 |
L. Braun and T. P. Vogels, “Online learning of neural computations from sparse temporal feedback,” in Advances in Neural Information Processing Systems - 35th Conference on Neural Information Processing Systems, Virtual, Online, 2021, vol. 20, pp. 16437–16450.
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2021 | Published | Conference Paper | IST-REx-ID: 11452 |
F. Alimisis, P. Davies, B. Vandereycken, and D.-A. Alistarh, “Distributed principal component analysis with limited communication,” in Advances in Neural Information Processing Systems - 35th Conference on Neural Information Processing Systems, Virtual, Online, 2021, vol. 4, pp. 2823–2834.
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| arXiv
2021 | Published | Conference Paper | IST-REx-ID: 10593 |
M. Mondelli and R. Venkataramanan, “PCA initialization for approximate message passing in rotationally invariant models,” in 35th Conference on Neural Information Processing Systems, Virtual, 2021, vol. 35, pp. 29616–29629.
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| arXiv
2021 | Published | Conference Paper | IST-REx-ID: 10594 |
Q. Nguyen, P. Bréchet, and M. Mondelli, “When are solutions connected in deep networks?,” in 35th Conference on Neural Information Processing Systems, Virtual, 2021, vol. 35.
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| arXiv
2021 | Published | Conference Paper | IST-REx-ID: 11458 |
A. Krumes, E. B. Iofinova, A. Vladu, and D.-A. Alistarh, “AC/DC: Alternating Compressed/DeCompressed training of deep neural networks,” in 35th Conference on Neural Information Processing Systems, Virtual, Online, 2021, vol. 34, pp. 8557–8570.
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| arXiv
2021 | Published | Conference Paper | IST-REx-ID: 11463 |
E. Frantar, E. Kurtic, and D.-A. Alistarh, “M-FAC: Efficient matrix-free approximations of second-order information,” in 35th Conference on Neural Information Processing Systems, Virtual, Online, 2021, vol. 34, pp. 14873–14886.
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| arXiv
2021 | Published | Conference Paper | IST-REx-ID: 11464 |
D.-A. Alistarh and J. Korhonen, “Towards tight communication lower bounds for distributed optimisation,” in 35th Conference on Neural Information Processing Systems, Virtual, Online, 2021, vol. 34, pp. 7254–7266.
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| arXiv
2020 | Published | Conference Paper | IST-REx-ID: 9632 |
S. P. Singh and D.-A. Alistarh, “WoodFisher: Efficient second-order approximation for neural network compression,” presented at the NeurIPS: Conference on Neural Information Processing Systems, Vancouver, Canada, 2020, vol. 33, pp. 18098–18109.
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| arXiv
2020 | Published | Conference Paper | IST-REx-ID: 9631 |
V. Aksenov, D.-A. Alistarh, and J. Korhonen, “Scalable belief propagation via relaxed scheduling,” presented at the NeurIPS: Conference on Neural Information Processing Systems, Vancouver, Canada, 2020, vol. 33, pp. 22361–22372.
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| arXiv
2020 | Published | Conference Paper | IST-REx-ID: 9633 |
B. J. Confavreux, F. Zenke, E. J. Agnes, T. Lillicrap, and T. P. Vogels, “A meta-learning approach to (re)discover plasticity rules that carve a desired function into a neural network,” in Advances in Neural Information Processing Systems, Vancouver, Canada, 2020, vol. 33, pp. 16398–16408.
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