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Poster
Thu 14:00 Memorization and Optimization in Deep Neural Networks with Minimum Over-parameterization
Simone Bombari · Mohammad Hossein Amani · Marco Mondelli
Poster
Thu 9:00 Thinking Outside the Ball: Optimal Learning with Gradient Descent for Generalized Linear Stochastic Convex Optimization
Idan Amir · Roi Livni · Nati Srebro
Poster
Wed 9:00 Revisit last-iterate convergence of mSGD under milder requirement on step size
ruinan Jin · Xingkang He · Lang Chen · Difei Cheng · Vijay Gupta
Poster
Thu 9:00 Accelerating SGD for Highly Ill-Conditioned Huge-Scale Online Matrix Completion
Jialun Zhang · Hong-Ming Chiu · Richard Y Zhang
Poster
Wed 9:00 Constrained Langevin Algorithms with L-mixing External Random Variables
Yuping Zheng · Andrew Lamperski
Poster
Wed 14:00 Sampling in Constrained Domains with Orthogonal-Space Variational Gradient Descent
Ruqi Zhang · Qiang Liu · Xin Tong
Poster
Tue 9:00 Learning single-index models with shallow neural networks
Alberto Bietti · Joan Bruna · Clayton Sanford · Min Jae Song
Workshop
Generalization of Decentralized Gradient Descent with Separable Data
Hossein Taheri · Christos Thrampoulidis
Poster
Thu 14:00 On the Interpretability of Regularisation for Neural Networks Through Model Gradient Similarity
Vincent Szolnoky · Viktor Andersson · Balazs Kulcsar · Rebecka Jörnsten
Poster
Wed 14:00 The alignment property of SGD noise and how it helps select flat minima: A stability analysis
Lei Wu · Mingze Wang · Weijie Su
Poster
Wed 14:00 Stochastic Multiple Target Sampling Gradient Descent
Hoang Phan · Ngoc Tran · Trung Le · Toan Tran · Nhat Ho · Dinh Phung
Poster
Wed 9:00 Risk Bounds of Multi-Pass SGD for Least Squares in the Interpolation Regime
Difan Zou · Jingfeng Wu · Vladimir Braverman · Quanquan Gu · Sham Kakade