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Poster
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Wed 9:00
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Generalization Bounds for Stochastic Gradient Descent via Localized ε-Covers
Sejun Park · Umut Simsekli · Murat Erdogdu
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Panel
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Tue 10:15
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Panel 1A-3: A gradient sampling… & Local Bayesian optimization…
Swati Padmanabhan · Quan Nguyen
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Poster
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Thu 14:00
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Memorization and Optimization in Deep Neural Networks with Minimum Over-parameterization
Simone Bombari · Mohammad Hossein Amani · Marco Mondelli
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Workshop
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Generalization of Decentralized Gradient Descent with Separable Data
Hossein Taheri · Christos Thrampoulidis
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Poster
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Wed 14:00
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Sampling in Constrained Domains with Orthogonal-Space Variational Gradient Descent
Ruqi Zhang · Qiang Liu · Xin Tong
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Poster
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Wed 9:00
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Constrained Langevin Algorithms with L-mixing External Random Variables
Yuping Zheng · Andrew Lamperski
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Poster
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Wed 9:00
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Revisit last-iterate convergence of mSGD under milder requirement on step size
ruinan Jin · Xingkang He · Lang Chen · Difei Cheng · Vijay Gupta
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Poster
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Thu 9:00
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Accelerating SGD for Highly Ill-Conditioned Huge-Scale Online Matrix Completion
Jialun Zhang · Hong-Ming Chiu · Richard Y Zhang
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Poster
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Tue 9:00
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Learning single-index models with shallow neural networks
Alberto Bietti · Joan Bruna · Clayton Sanford · Min Jae Song
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Poster
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Thu 9:00
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Thinking Outside the Ball: Optimal Learning with Gradient Descent for Generalized Linear Stochastic Convex Optimization
Idan Amir · Roi Livni · Nati Srebro
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Poster
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Thu 14:00
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On the Interpretability of Regularisation for Neural Networks Through Model Gradient Similarity
Vincent Szolnoky · Viktor Andersson · Balazs Kulcsar · Rebecka Jörnsten
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Poster
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Wed 14:00
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The alignment property of SGD noise and how it helps select flat minima: A stability analysis
Lei Wu · Mingze Wang · Weijie Su
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