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Disambiguation of "Nitanda, Atsushi"

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Why is parameter averaging beneficial in SGD? An objective smoothing perspective.

, , , and . AISTATS, volume 238 of Proceedings of Machine Learning Research, page 3565-3573. PMLR, (2024)

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Atsushi Imai

Atsushi Imai

Atsushi Fukui

Atsushi Yagi

Atsushi Kambayashi

 

Other publications of authors with the same name

Uniform-in-time propagation of chaos for the mean-field gradient Langevin dynamics., , and . ICLR, OpenReview.net, (2023)Koopman-based generalization bound: New aspect for full-rank weights., , , , and . ICLR, OpenReview.net, (2024)Stochastic Gradient Descent with Exponential Convergence Rates of Expected Classification Errors., and . AISTATS, volume 89 of Proceedings of Machine Learning Research, page 1417-1426. PMLR, (2019)Accelerated Stochastic Gradient Descent for Minimizing Finite Sums.. AISTATS, volume 51 of JMLR Workshop and Conference Proceedings, page 195-203. JMLR.org, (2016)Exponential Convergence Rates of Classification Errors on Learning with SGD and Random Features., , and . AISTATS, volume 130 of Proceedings of Machine Learning Research, page 1954-1962. PMLR, (2021)Convex Analysis of the Mean Field Langevin Dynamics., , and . AISTATS, volume 151 of Proceedings of Machine Learning Research, page 9741-9757. PMLR, (2022)Generalization Bounds for Graph Embedding Using Negative Sampling: Linear vs Hyperbolic., , , , , and . NeurIPS, page 1243-1255. (2021)Data Cleansing for Models Trained with SGD., , and . NeurIPS, page 4215-4224. (2019)Accelerated Stochastic Gradient Descent for Minimizing Finite Sums.. CoRR, (2015)Hyperbolic Ordinal Embedding., , , , and . ACML, volume 101 of Proceedings of Machine Learning Research, page 1065-1080. PMLR, (2019)