86 citations · 120 across the 18 of their papers we have counts for
3 papers · 1 filter
Gap-Dependent Unsupervised Exploration for Reinforcement Learning
Jingfeng Wu, Vladimir Braverman, Lin F. Yang
For the problem of task-agnostic reinforcement learning (RL), an agent first collects samples from an unknown environment without the supervision of reward signals, then is reveale…
The Benefits of Implicit Regularization from SGD in Least Squares Problems
Difan Zou, Jingfeng Wu, Vladimir Braverman +3
Stochastic gradient descent (SGD) exhibits strong algorithmic regularization effects in practice, which has been hypothesized to play an important role in the generalization of mod…
Benign Overfitting of Constant-Stepsize SGD for Linear Regression
Difan Zou, Jingfeng Wu, Vladimir Braverman +2
There is an increasing realization that algorithmic inductive biases are central in preventing overfitting; empirically, we often see a benign overfitting phenomenon in overparamet…