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11 Results
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Spotlight
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Wed 5:15 |
Benefit of deep learning with non-convex noisy gradient descent: Provable excess risk bound and superiority to kernel methods Taiji Suzuki · Akiyama Shunta |
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Poster
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Mon 1:00 |
Wasserstein-2 Generative Networks Alexander Korotin · Vage Egiazarian · Arip Asadulaev · Alexander Safin · Evgeny Burnaev |
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Poster
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Wed 9:00 |
Continuous Wasserstein-2 Barycenter Estimation without Minimax Optimization Alexander Korotin · Lingxiao Li · Justin Solomon · Evgeny Burnaev |
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Poster
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Mon 17:00 |
Benefit of deep learning with non-convex noisy gradient descent: Provable excess risk bound and superiority to kernel methods Taiji Suzuki · Akiyama Shunta |
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Poster
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Mon 17:00 |
Learning A Minimax Optimizer: A Pilot Study Jiayi Shen · Xiaohan Chen · Howard Heaton · Tianlong Chen · Jialin Liu · Wotao Yin · Zhangyang Wang |
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Poster
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Wed 17:00 |
AutoLRS: Automatic Learning-Rate Schedule by Bayesian Optimization on the Fly Yuchen Jin · Tianyi Zhou · Liangyu Zhao · Yibo Zhu · Chuanxiong Guo · Marco Canini · Arvind Krishnamurthy |
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Poster
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Thu 1:00 |
AdamP: Slowing Down the Slowdown for Momentum Optimizers on Scale-invariant Weights Byeongho Heo · Sanghyuk Chun · Seong Joon Oh · Dongyoon Han · Sangdoo Yun · Gyuwan Kim · Youngjung Uh · Jung-Woo Ha |
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Poster
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Tue 9:00 |
On the Origin of Implicit Regularization in Stochastic Gradient Descent Samuel Smith · Benoit Dherin · David Barrett · Soham De |
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Oral
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Thu 0:30 |
Optimal Rates for Averaged Stochastic Gradient Descent under Neural Tangent Kernel Regime Atsushi Nitanda · Taiji Suzuki |
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Poster
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Thu 1:00 |
Optimal Rates for Averaged Stochastic Gradient Descent under Neural Tangent Kernel Regime Atsushi Nitanda · Taiji Suzuki |
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Poster
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Mon 1:00 |
Overfitting for Fun and Profit: Instance-Adaptive Data Compression Ties van Rozendaal · Iris Huijben · Taco Cohen |