12 papers
HeteroFedSyn: Differentially Private Tabular Data Synthesis for Heterogeneous Federated Settings
Xiaochen Li, Fengyu Gao, Xizixiang Wei +3
Traditional Differential Privacy (DP) mechanisms are typically tailored to specific analysis tasks, which limits the reusability of protected data. DP tabular data synthesis overco…
Tele-LLM-Hub: Building Context-Aware Multi-Agent LLM Systems for Telecom Networks
Pranshav Gajjar, Cong Shen, Vijay K Shah
This paper introduces Tele-LLM-Hub, a user friendly low-code solution for rapid prototyping and deployment of context aware multi-agent (MA) Large Language Model (LLM) systems tail…
Chain-of-Thought Enhanced Shallow Transformers for Wireless Symbol Detection
Li Fan, Peng Wang, Jing Yang +1
Transformers have shown potential in solving wireless communication problems, particularly via in-context learning (ICL), where models adapt to new tasks through prompts without re…
A Shared Low-Rank Adaptation Approach to Personalized RLHF
Renpu Liu, Peng Wang, Donghao Li +2
Reinforcement Learning from Human Feedback (RLHF) has emerged as a pivotal technique for aligning artificial intelligence systems with human values, achieving remarkable success in…
Cost-Aware Optimal Pairwise Pure Exploration
Di Wu, Chengshuai Shi, Ruida Zhou +1
Pure exploration is one of the fundamental problems in multi-armed bandits (MAB). However, existing works mostly focus on specific pure exploration tasks, without a holistic view o…
Decision Feedback In-Context Learning for Wireless Symbol Detection
Li Fan, Wei Shen, Jing Yang +1
Pre-trained Transformers, through in-context learning (ICL), have demonstrated exceptional capabilities to adapt to new tasks using example prompts without model update. Transforme…
Average Reward Reinforcement Learning for Wireless Radio Resource Management
Kun Yang, Jing Yang, Cong Shen
In this paper, we address a crucial but often overlooked issue in applying reinforcement learning (RL) to radio resource management (RRM) in wireless communications: the mismatch b…
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…
Data-adaptive Differentially Private Prompt Synthesis for In-Context Learning
Fengyu Gao, Ruida Zhou, Tianhao Wang +2
Large Language Models (LLMs) rely on the contextual information embedded in examples/demonstrations to perform in-context learning (ICL). To mitigate the risk of LLMs potentially l…
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…