14 citations · 18 across the 3 of their papers we have counts for
3 papers
cs.LG2021★ 2 cited
Gradient Descent on Two-layer Nets: Margin Maximization and Simplicity Bias
Kaifeng Lyu, Zhiyuan Li, Runzhe Wang +1
The generalization mystery of overparametrized deep nets has motivated efforts to understand how gradient descent (GD) converges to low-loss solutions that generalize well. Real-li…
cs.LG2021★ 2 cited
Optimal Gradient-based Algorithms for Non-concave Bandit Optimization
Baihe Huang, Kaixuan Huang, Sham M. Kakade +4
Bandit problems with linear or concave reward have been extensively studied, but relatively few works have studied bandits with non-concave reward. This work considers a large fami…
cs.LG2019★ 14 cited
Mildly Overparametrized Neural Nets can Memorize Training Data Efficiently
Rong Ge, Runzhe Wang, Haoyu Zhao
It has been observed \citep{zhang2016understanding} that deep neural networks can memorize: they achieve 100\% accuracy on training data. Recent theoretical results explained such…