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Workshop
Wed 16:30 Easing non-convex optimization with neural networks
David Lopez-Paz · Levent Sagun
Poster
Wed 14:30 Subgradient Descent Learns Orthogonal Dictionaries
Yu Bai · Qijia Jiang · Ju Sun
Poster
Wed 14:30 Gradient Descent Provably Optimizes Over-parameterized Neural Networks
Simon Du · Xiyu Zhai · Barnabás Póczos · Aarti Singh
Poster
Wed 14:30 Universal Stagewise Learning for Non-Convex Problems with Convergence on Averaged Solutions
Zaiyi Chen · Zhuoning Yuan · Jinfeng Yi · Bowen Zhou · Enhong Chen · Tianbao Yang
Poster
Wed 14:30 On the Convergence of A Class of Adam-Type Algorithms for Non-Convex Optimization
Xiangyi Chen · Sijia Liu · Ruoyu Sun · Mingyi Hong
Poster
Wed 14:30 A Convergence Analysis of Gradient Descent for Deep Linear Neural Networks
Sanjeev Arora · Nadav Cohen · Noah Golowich · Wei Hu
Poster
Thu 9:00 Stable Recurrent Models
John Miller · Moritz Hardt
Spotlight
Mon 12:15 On the Theory of Implicit Deep Learning: Global Convergence with Implicit Layers
Kenji Kawaguchi
Poster
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
Poster
Tue 9:00 On the Theory of Implicit Deep Learning: Global Convergence with Implicit Layers
Kenji Kawaguchi
Poster
Tue 9:00 NOVAS: Non-convex Optimization via Adaptive Stochastic Search for End-to-end Learning and Control
Ioannis Exarchos · Marcus A Pereira · Ziyi Wang · Evangelos Theodorou
Spotlight
Tue 12:40 Implicit Convex Regularizers of CNN Architectures: Convex Optimization of Two- and Three-Layer Networks in Polynomial Time
Tolga Ergen · Mert Pilanci