|
Poster
|
Mon 17:00
|
Proximal Gradient Descent-Ascent: Variable Convergence under KŁ Geometry
Ziyi Chen · Yi Zhou · Tengyu Xu · Yingbin Liang
|
|
|
Spotlight
|
Wed 20:20
|
Understanding the role of importance weighting for deep learning
Da Xu · Yuting Ye · Chuanwei Ruan
|
|
|
Poster
|
Tue 17:00
|
Understanding the role of importance weighting for deep learning
Da Xu · Yuting Ye · Chuanwei Ruan
|
|
|
Oral
|
Thu 0:30
|
Optimal Rates for Averaged Stochastic Gradient Descent under Neural Tangent Kernel Regime
Atsushi Nitanda · Taiji Suzuki
|
|
|
Poster
|
Thu 1:00
|
Optimal Rates for Averaged Stochastic Gradient Descent under Neural Tangent Kernel Regime
Atsushi Nitanda · Taiji Suzuki
|
|
|
Poster
|
Tue 17:00
|
Gradient Descent on Neural Networks Typically Occurs at the Edge of Stability
Jeremy Cohen · Simran Kaur · Yuanzhi Li · Zico Kolter · Ameet Talwalkar
|
|
|
Poster
|
Thu 1:00
|
The inductive bias of ReLU networks on orthogonally separable data
Mary Phuong · Christoph H Lampert
|
|
|
Poster
|
Thu 9:00
|
Linear Last-iterate Convergence in Constrained Saddle-point Optimization
Chen-Yu Wei · Chung-Wei Lee · Mengxiao Zhang · Haipeng Luo
|
|
|
Poster
|
Mon 9:00
|
On the Impossibility of Global Convergence in Multi-Loss Optimization
Alistair Letcher
|
|
|
Poster
|
Tue 9:00
|
On the Theory of Implicit Deep Learning: Global Convergence with Implicit Layers
Kenji Kawaguchi
|
|
|
Poster
|
Tue 9:00
|
Direction Matters: On the Implicit Bias of Stochastic Gradient Descent with Moderate Learning Rate
Jingfeng Wu · Difan Zou · vladimir braverman · Quanquan Gu
|
|
|
Spotlight
|
Mon 12:15
|
On the Theory of Implicit Deep Learning: Global Convergence with Implicit Layers
Kenji Kawaguchi
|
|
