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01/27/2023
Understanding Incremental Learning of Gradient Descent: A Fine-grained Analysis of Matrix Sensing
It is believed that Gradient Descent (GD) induces an implicit bias towar...
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09/28/2022
Minimax Optimal Kernel Operator Learning via Multilevel Training
Learning mappings between infinite-dimensional function spaces has achie...
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05/27/2022
Why Robust Generalization in Deep Learning is Difficult: Perspective of Expressive Power
It is well-known that modern neural networks are vulnerable to adversari...
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11/04/2021
A Riemannian Accelerated Proximal Extragradient Framework and its Implications
The study of accelerated gradient methods in Riemannian optimization has...
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10/24/2021
Non-convex Distributionally Robust Optimization: Non-asymptotic Analysis
Distributionally robust optimization (DRO) is a widely-used approach to ...
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10/10/2020
On The Convergence of First Order Methods for Quasar-Convex Optimization
In recent years, the success of deep learning has inspired many research...
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10/05/2020
