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Collaborations and top research areas from the last five years
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In-depth Analysis of Low-rank Matrix Factorisation in a Federated Setting
Philippenko, C., Scaman, K. & Massoulié, L., 11 Apr 2025, Special Track on AI Alignment. Walsh, T., Shah, J. & Kolter, Z. (eds.). 19 ed. Association for the Advancement of Artificial Intelligence, p. 19904-19912 9 p. (Proceedings of the AAAI Conference on Artificial Intelligence; vol. 39, no. 19).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review
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When to Forget? Complexity Trade-offs in Machine Unlearning
Van Waerebeke, M., Lorenzi, M., Neglia, G. & Scaman, K., 1 Jan 2025, In: Proceedings of Machine Learning Research. 267, p. 60816-60832 17 p.Research output: Contribution to journal › Conference article › peer-review
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Improved Stability and Generalization Guarantees of the Decentralized SGD Algorithm
Le Bars, B., Bellet, A., Tommasi, M., Scaman, K. & Neglia, G., 1 Jan 2024, In: Proceedings of Machine Learning Research. 235, p. 26215-26240 26 p.Research output: Contribution to journal › Conference article › peer-review
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Minimax Excess Risk of First-Order Methods for Statistical Learning with Data-Dependent Oracles
Scaman, K., Even, M., Le Bars, B. & Massoulié, L., 1 Jan 2024, In: Proceedings of Machine Learning Research. 238, p. 3709-3717 9 p.Research output: Contribution to journal › Conference article › peer-review
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RANDOM SPARSE LIFTS: CONSTRUCTION, ANALYSIS AND CONVERGENCE OF FINITE SPARSE NETWORKS
Robin, D. A. R., Scaman, K. & Lelarge, M., 1 Jan 2024.Research output: Contribution to conference › Paper › peer-review
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SIFU: Sequential Informed Federated Unlearning for Efficient and Provable Client Unlearning in Federated Optimization
Fraboni, Y., Van Waerebeke, M., Vidal, R., Kameni, L., Scaman, K. & Lorenzi, M., 1 Jan 2024, In: Proceedings of Machine Learning Research. 238, p. 3457-3465 9 p.Research output: Contribution to journal › Conference article › peer-review
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Periodic Signal Recovery with Regularized Sine Neural Networks
Robin, D. A. R., Scaman, K. & Lelarge, M., 1 Jan 2023, In: Proceedings of Machine Learning Research. 197, p. 98-110 13 p.Research output: Contribution to journal › Conference article › peer-review
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Convergence beyond the over-parameterized regime using Rayleigh quotients
Robin, D. A. R., Scaman, K. & Lelarge, M., 1 Jan 2022, Advances in Neural Information Processing Systems 35 - 36th Conference on Neural Information Processing Systems, NeurIPS 2022. Koyejo, S., Mohamed, S., Agarwal, A., Belgrave, D., Cho, K. & Oh, A. (eds.). Neural information processing systems foundation, (Advances in Neural Information Processing Systems; vol. 35).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review
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Convergence Rates of Non-Convex Stochastic Gradient Descent Under a Generic Łojasiewicz Condition and Local Smoothness
Scaman, K., Malherbe, C. & Dos Santos, L., 1 Jan 2022, In: Proceedings of Machine Learning Research. 162, p. 19310-19327 18 p.Research output: Contribution to journal › Conference article › peer-review
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On Sample Optimality in Personalized Collaborative and Federated Learning
Even, M., Massoulié, L. & Scaman, K., 1 Jan 2022, Advances in Neural Information Processing Systems 35 - 36th Conference on Neural Information Processing Systems, NeurIPS 2022. Koyejo, S., Mohamed, S., Agarwal, A., Belgrave, D., Cho, K. & Oh, A. (eds.). Neural information processing systems foundation, (Advances in Neural Information Processing Systems; vol. 35).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review