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Workshop
Stochastic Gradient Descent-Ascent: Unified Theory and New Efficient Methods
Aleksandr Beznosikov · Eduard Gorbunov · Hugo Berard · Nicolas Loizou
Poster
Wed 14:00 Clipped Stochastic Methods for Variational Inequalities with Heavy-Tailed Noise
Eduard Gorbunov · Marina Danilova · David Dobre · Pavel Dvurechenskii · Alexander Gasnikov · Gauthier Gidel
Poster
Wed 14:00 Regularized Gradient Descent Ascent for Two-Player Zero-Sum Markov Games
Sihan Zeng · Thinh Doan · Justin Romberg
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 Accelerating SGD for Highly Ill-Conditioned Huge-Scale Online Matrix Completion
Jialun Zhang · Hong-Ming Chiu · Richard Y Zhang
Poster
Tue 9:00 Learning single-index models with shallow neural networks
Alberto Bietti · Joan Bruna · Clayton Sanford · Min Jae Song
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 9:00 Constrained Langevin Algorithms with L-mixing External Random Variables
Yuping Zheng · Andrew Lamperski
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 Thinking Outside the Ball: Optimal Learning with Gradient Descent for Generalized Linear Stochastic Convex Optimization
Idan Amir · Roi Livni · Nati Srebro
Poster
Wed 14:00 Sampling in Constrained Domains with Orthogonal-Space Variational Gradient Descent
Ruqi Zhang · Qiang Liu · Xin Tong
Workshop
Generalization of Decentralized Gradient Descent with Separable Data
Hossein Taheri · Christos Thrampoulidis