15 citations · 60 across the 31 of their papers we have counts for
6 papers · 1 filter
Decision Feedback In-Context Symbol Detection over Block-Fading Channels
Li Fan, Jing Yang, Cong Shen
Pre-trained Transformers, through in-context learning (ICL), have demonstrated exceptional capabilities to adapt to new tasks using example prompts \textit{without model update}. T…
Transformers as Game Players: Provable In-context Game-playing Capabilities of Pre-trained Models
Chengshuai Shi, Kun Yang, Jing Yang +1
The in-context learning (ICL) capability of pre-trained models based on the transformer architecture has received growing interest in recent years. While theoretical understanding…
On the Learn-to-Optimize Capabilities of Transformers in In-Context Sparse Recovery
Renpu Liu, Ruida Zhou, Cong Shen +1
An intriguing property of the Transformer is its ability to perform in-context learning (ICL), where the Transformer can solve different inference tasks without parameter updating…
On the Training Convergence of Transformers for In-Context Classification of Gaussian Mixtures
Wei Shen, Ruida Zhou, Jing Yang +1
Although transformers have demonstrated impressive capabilities for in-context learning (ICL) in practice, theoretical understanding of the underlying mechanism that allows transfo…
Federated Representation Learning in the Under-Parameterized Regime
Renpu Liu, Cong Shen, Jing Yang
Federated representation learning (FRL) is a popular personalized federated learning (FL) framework where clients work together to train a common representation while retaining the…
Efficient Prompt Optimization Through the Lens of Best Arm Identification
Chengshuai Shi, Kun Yang, Zihan Chen +3
The remarkable instruction-following capability of large language models (LLMs) has sparked a growing interest in automatically finding good prompts, i.e., prompt optimization. Mos…