4 citations · 6 across the 6 of their papers we have counts for
6 papers
Rubick: Exploiting Job Reconfigurability for Deep Learning Cluster Scheduling
Xinyi Zhang, Hanyu Zhao, Wencong Xiao +5
The era of large deep learning models has given rise to advanced training strategies such as 3D parallelism and the ZeRO series. These strategies enable various (re-)configurable e…
Llumnix: Dynamic Scheduling for Large Language Model Serving
Biao Sun, Ziming Huang, Hanyu Zhao +4
Inference serving for large language models (LLMs) is the key to unleashing their potential in people's daily lives. However, efficient LLM serving remains challenging today becaus…
Privacy in LLM-based Recommendation: Recent Advances and Future Directions
Sichun Luo, Wei Shao, Yuxuan Yao +9
Nowadays, large language models (LLMs) have been integrated with conventional recommendation models to improve recommendation performance. However, while most of the existing works…
Biased Binary Attribute Classifiers Ignore the Majority Classes
Xinyi Zhang, Johanna Sophie Bieri, Manuel Günther
To visualize the regions of interest that classifiers base their decisions on, different Class Activation Mapping (CAM) methods have been developed. However, all of these technique…
PerFedRec++: Enhancing Personalized Federated Recommendation with Self-Supervised Pre-Training
Sichun Luo, Yuanzhang Xiao, Xinyi Zhang +3
Federated recommendation systems employ federated learning techniques to safeguard user privacy by transmitting model parameters instead of raw user data between user devices and t…
A Unified and Efficient Coordinating Framework for Autonomous DBMS Tuning
Xinyi Zhang, Zhuo Chang, Hong Wu +5
Recently using machine learning (ML) based techniques to optimize modern database management systems has attracted intensive interest from both industry and academia. With an objec…