1 citations · 1 across the 2 of their papers we have counts for
5 papers
RoSTE: An Efficient Quantization-Aware Supervised Fine-Tuning Approach for Large Language Models
Quan Wei, Chung-Yiu Yau, Hoi-To Wai +4
Supervised fine-tuning is a standard method for adapting pre-trained large language models (LLMs) to downstream tasks. Quantization has been recently studied as a post-training tec…
Decentralized Stochastic Optimization over Unreliable Networks via Two-timescales Updates
Haoming Liu, Chung-Yiu Yau, Hoi-To Wai
This paper introduces a robust two-timescale compressed primal-dual (TiCoPD) algorithm tailored for decentralized optimization under bandwidth-limited and unreliable channels. By i…
A Two-timescale Primal-dual Algorithm for Decentralized Optimization with Compression
Haoming Liu, Chung-Yiu Yau, Hoi-To Wai
This paper proposes a two-timescale compressed primal-dual (TiCoPD) algorithm for decentralized optimization with improved communication efficiency over prior works on primal-dual…
A Stochastic Approximation Approach for Efficient Decentralized Optimization on Random Networks
Chung-Yiu Yau, Haoming Liu, Hoi-To Wai
A challenging problem in decentralized optimization is to develop algorithms with fast convergence on random and time varying topologies under unreliable and bandwidth-constrained…
Multi-agent Performative Prediction with Greedy Deployment and Consensus Seeking Agents
Qiang Li, Chung-Yiu Yau, Hoi-To Wai
We consider a scenario where multiple agents are learning a common decision vector from data which can be influenced by the agents' decisions. This leads to the problem of multi-ag…