6 papers
Policy Optimization Achieves Data-Dependent Regret Bounds in MDPs with Unknown Transitions
Mingyi Li, Taira Tsuchiya, Kenji Yamanishi
We study policy optimization for online episodic tabular Markov decision processes with unknown transition kernels, aiming for best-of-both-worlds guarantees together with data-dep…
Data- and Variance-dependent Regret Bounds for Online Tabular MDPs
Mingyi Li, Taira Tsuchiya, Kenji Yamanishi
This work studies online episodic tabular Markov decision processes (MDPs) with known transitions and develops best-of-both-worlds algorithms that achieve refined data-dependent re…
Distributed Bilevel Optimization with Dual Pruning for Resource-limited Clients
Mingyi Li, Xiao Zhang, Ruisheng Zheng +4
With the development of large-scale models, traditional distributed bilevel optimization algorithms cannot be applied directly in low-resource clients. The key reason lies in the e…
TAMO: Fine-Grained Root Cause Analysis via Tool-Assisted LLM Agent with Multi-Modality Observation Data in Cloud-Native Systems
Xiao Zhang, Qi Wang, Mingyi Li +4
Implementing large language models (LLMs)-driven root cause analysis (RCA) in cloud-native systems has become a key topic of modern software operations and maintenance. However, ex…
Unity is Power: Semi-Asynchronous Collaborative Training of Large-Scale Models with Structured Pruning in Resource-Limited Clients
Yan Li, Xiao Zhang, Mingyi Li +7
In this work, we study to release the potential of massive heterogeneous weak computing power to collaboratively train large-scale models on dispersed datasets. In order to improve…
Modified K-means Algorithm with Local Optimality Guarantees
Mingyi Li, Michael R. Metel, Akiko Takeda
The K-means algorithm is one of the most widely studied clustering algorithms in machine learning. While extensive research has focused on its ability to achieve a globally optimal…