274 citations · 3.1k across the 75 of their papers we have counts for
4 papers · 1 filter
Towards Understanding the Data Dependency of Mixup-style Training
Muthu Chidambaram, Xiang Wang, Yuzheng Hu +2
In the Mixup training paradigm, a model is trained using convex combinations of data points and their associated labels. Despite seeing very few true data points during training, m…
Outlier-Robust Sparse Estimation via Non-Convex Optimization
Yu Cheng, Ilias Diakonikolas, Rong Ge +3
We explore the connection between outlier-robust high-dimensional statistics and non-convex optimization in the presence of sparsity constraints, with a focus on the fundamental ta…
Understanding Deflation Process in Over-parametrized Tensor Decomposition
Rong Ge, Yunwei Ren, Xiang Wang +1
In this paper we study the training dynamics for gradient flow on over-parametrized tensor decomposition problems. Empirically, such training process often first fits larger compon…
A Local Convergence Theory for Mildly Over-Parameterized Two-Layer Neural Network
Mo Zhou, Rong Ge, Chi Jin
While over-parameterization is widely believed to be crucial for the success of optimization for the neural networks, most existing theories on over-parameterization do not fully e…