3 citations · 6 across the 4 of their papers we have counts for
6 papers · 1 filter
What can a Single Attention Layer Learn? A Study Through the Random Features Lens
Hengyu Fu, Tianyu Guo, Yu Bai +1
Attention layers -- which map a sequence of inputs to a sequence of outputs -- are core building blocks of the Transformer architecture which has achieved significant breakthroughs…
Sample-Efficient Learning of POMDPs with Multiple Observations In Hindsight
Jiacheng Guo, Minshuo Chen, Huan Wang +3
This paper studies the sample-efficiency of learning in Partially Observable Markov Decision Processes (POMDPs), a challenging problem in reinforcement learning that is known to be…
Transformers as Statisticians: Provable In-Context Learning with In-Context Algorithm Selection
Yu Bai, Fan Chen, Huan Wang +2
Neural sequence models based on the transformer architecture have demonstrated remarkable \emph{in-context learning} (ICL) abilities, where they can perform new tasks when prompted…
The Role of Coverage in Online Reinforcement Learning
Tengyang Xie, Dylan J. Foster, Yu Bai +2
Coverage conditions -- which assert that the data logging distribution adequately covers the state space -- play a fundamental role in determining the sample complexity of offline…
Sample-Efficient Learning of Correlated Equilibria in Extensive-Form Games
Ziang Song, Song Mei, Yu Bai
Imperfect-Information Extensive-Form Games (IIEFGs) is a prevalent model for real-world games involving imperfect information and sequential plays. The Extensive-Form Correlated Eq…
Efficient and Differentiable Conformal Prediction with General Function Classes
Yu Bai, Song Mei, Huan Wang +2
Quantifying the data uncertainty in learning tasks is often done by learning a prediction interval or prediction set of the label given the input. Two commonly desired properties f…