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03/07/2023
Benign Overfitting for Two-layer ReLU Networks
Modern deep learning models with great expressive power can be trained t...
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03/03/2023
Learning High-Dimensional Single-Neuron ReLU Networks with Finite Samples
This paper considers the problem of learning a single ReLU neuron with s...
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11/04/2022
ISA-Net: Improved spatial attention network for PET-CT tumor segmentation
Achieving accurate and automated tumor segmentation plays an important r...
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09/30/2022
A General Framework for Sample-Efficient Function Approximation in Reinforcement Learning
With the increasing need for handling large state and action spaces, gen...
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08/04/2022
Towards Understanding Mixture of Experts in Deep Learning
The Mixture-of-Experts (MoE) layer, a sparsely-activated model controlle...
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02/14/2022
Benign Overfitting in Two-layer Convolutional Neural Networks
Modern neural networks often have great expressive power and can be trai...
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10/25/2021
Faster Perturbed Stochastic Gradient Methods for Finding Local Minima
Escaping from saddle points and finding local minima is a central proble...
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06/25/2021
Self-training Converts Weak Learners to Strong Learners in Mixture Models
We consider a binary classification problem when the data comes from a m...
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02/15/2021
Almost Optimal Algorithms for Two-player Markov Games with Linear Function Approximation
We study reinforcement learning for two-player zero-sum Markov games wit...
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11/27/2019
How Much Over-parameterization Is Sufficient to Learn Deep ReLU Networks?
A recent line of research on deep learning focuses on the extremely over...
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10/08/2018