384 citations · 1.3k across the 26 of their papers we have counts for
5 papers · 1 filter
Impact of Representation Learning in Linear Bandits
Jiaqi Yang, Wei Hu, Jason D. Lee +1
We study how representation learning can improve the efficiency of bandit problems. We study the setting where we play linear bandits with dimension concurrently, and these…
The Surprising Simplicity of the Early-Time Learning Dynamics of Neural Networks
Wei Hu, Lechao Xiao, Ben Adlam +1
Modern neural networks are often regarded as complex black-box functions whose behavior is difficult to understand owing to their nonlinear dependence on the data and the nonconvex…
When is Particle Filtering Efficient for Planning in Partially Observed Linear Dynamical Systems?
Simon S. Du, Wei Hu, Zhiyuan Li +3
Particle filtering is a popular method for inferring latent states in stochastic dynamical systems, whose theoretical properties have been well studied in machine learning and stat…
Few-Shot Learning via Learning the Representation, Provably
Simon S. Du, Wei Hu, Sham M. Kakade +2
This paper studies few-shot learning via representation learning, where one uses source tasks with data per task to learn a representation in order to reduce the sample c…
Provable Benefit of Orthogonal Initialization in Optimizing Deep Linear Networks
Wei Hu, Lechao Xiao, Jeffrey Pennington
The selection of initial parameter values for gradient-based optimization of deep neural networks is one of the most impactful hyperparameter choices in deep learning systems, affe…