2 citations · 3 across the 12 of their papers we have counts for
6 papers · 1 filter
FedEve: On Bridging the Client Drift and Period Drift for Cross-device Federated Learning
Tao Shen, Zexi Li, Didi Zhu +3
Federated learning (FL) is a machine learning paradigm that allows multiple clients to collaboratively train a shared model without exposing their private data. Data heterogeneity…
Each Rank Could be an Expert: Single-Ranked Mixture of Experts LoRA for Multi-Task Learning
Ziyu Zhao, Yixiao Zhou, Zhi Zhang +10
Low-Rank Adaptation (LoRA) is widely used for adapting large language models (LLMs) to specific domains due to its efficiency and modularity. Meanwhile, vanilla LoRA struggles with…
Merging LoRAs like Playing LEGO: Pushing the Modularity of LoRA to Extremes Through Rank-Wise Clustering
Ziyu Zhao, Tao Shen, Didi Zhu +5
Low-Rank Adaptation (LoRA) has emerged as a popular technique for fine-tuning large language models (LLMs) to various domains due to its modular design and widespread availability…
IntOPE: Off-Policy Evaluation in the Presence of Interference
Yuqi Bai, Ziyu Zhao, Chenxin Lyu +2
Off-Policy Evaluation (OPE) is employed to assess the potential impact of a hypothetical policy using logged contextual bandit feedback, which is crucial in areas such as personali…
Retrieval-Augmented Mixture of LoRA Experts for Uploadable Machine Learning
Ziyu Zhao, Leilei Gan, Guoyin Wang +5
Low-Rank Adaptation (LoRA) offers an efficient way to fine-tune large language models (LLMs). Its modular and plug-and-play nature allows the integration of various domain-specific…
Learning Individual Treatment Effects under Heterogeneous Interference in Networks
Ziyu Zhao, Yuqi Bai, Kun Kuang +2
Estimates of individual treatment effects from networked observational data are attracting increasing attention these days. One major challenge in network scenarios is the violatio…