Search Results for author: Yuanzhang Xiao

Found 10 papers, 0 papers with code

Improved Quantization Strategies for Managing Heavy-tailed Gradients in Distributed Learning

no code implementations2 Feb 2024 Guangfeng Yan, Tan Li, Yuanzhang Xiao, Hanxu Hou, Linqi Song

We consider a general family of heavy-tail gradients that follow a power-law distribution, we aim to minimize the error resulting from quantization, thereby determining optimal values for two critical parameters: the truncation threshold and the quantization density.

Quantization

Truncated Non-Uniform Quantization for Distributed SGD

no code implementations2 Feb 2024 Guangfeng Yan, Tan Li, Yuanzhang Xiao, Congduan Li, Linqi Song

To address the communication bottleneck challenge in distributed learning, our work introduces a novel two-stage quantization strategy designed to enhance the communication efficiency of distributed Stochastic Gradient Descent (SGD).

Quantization

Integrating Large Language Models into Recommendation via Mutual Augmentation and Adaptive Aggregation

no code implementations25 Jan 2024 Sichun Luo, Yuxuan Yao, Bowei He, Yinya Huang, Aojun Zhou, Xinyi Zhang, Yuanzhang Xiao, Mingjie Zhan, Linqi Song

Conventional recommendation methods have achieved notable advancements by harnessing collaborative or sequential information from user behavior.

Data Augmentation

RecRanker: Instruction Tuning Large Language Model as Ranker for Top-k Recommendation

no code implementations26 Dec 2023 Sichun Luo, Bowei He, Haohan Zhao, Yinya Huang, Aojun Zhou, Zongpeng Li, Yuanzhang Xiao, Mingjie Zhan, Linqi Song

In this paper, we introduce RecRanker, tailored for instruction tuning LLM to serve as the \textbf{Ranker} for top-\textit{k} \textbf{Rec}ommendations.

In-Context Learning Language Modelling +3

Unsupervised Massive MIMO Channel Estimation with Dual-Path Knowledge-Aware Auto-Encoders

no code implementations30 May 2023 Zhiheng Guo, Yuanzhang Xiao, Xiang Chen

In this paper, an unsupervised deep learning framework based on dual-path model-driven variational auto-encoders (VAE) is proposed for angle-of-arrivals (AoAs) and channel estimation in massive MIMO systems.

PerFedRec++: Enhancing Personalized Federated Recommendation with Self-Supervised Pre-Training

no code implementations11 May 2023 Sichun Luo, Yuanzhang Xiao, Xinyi Zhang, Yang Liu, Wenbo Ding, Linqi Song

Each user learns a personalized model by combining the global federated model, the cluster-level federated model, and its own fine-tuned local model.

Federated Learning Graph Learning +3

Adaptive Top-K in SGD for Communication-Efficient Distributed Learning

no code implementations24 Oct 2022 Mengzhe Ruan, Guangfeng Yan, Yuanzhang Xiao, Linqi Song, Weitao Xu

This paper proposes a novel adaptive Top-K in SGD framework that enables an adaptive degree of sparsification for each gradient descent step to optimize the convergence performance by balancing the trade-off between communication cost and convergence error.

Towards Communication Efficient and Fair Federated Personalized Sequential Recommendation

no code implementations23 Aug 2022 Sichun Luo, Yuanzhang Xiao, Yang Liu, Congduan Li, Linqi Song

Federated recommendations leverage the federated learning (FL) techniques to make privacy-preserving recommendations.

Fairness Federated Learning +2

HySAGE: A Hybrid Static and Adaptive Graph Embedding Network for Context-Drifting Recommendations

no code implementations20 Aug 2022 Sichun Luo, Xinyi Zhang, Yuanzhang Xiao, Linqi Song

For example, in a mobile game recommendation, contextual features like locations, battery, and storage levels of mobile devices are frequently drifting over time.

Collaborative Filtering Graph Embedding

Personalized Federated Recommendation via Joint Representation Learning, User Clustering, and Model Adaptation

no code implementations19 Aug 2022 Sichun Luo, Yuanzhang Xiao, Linqi Song

In this paper, we propose a Graph Neural Network based Personalized Federated Recommendation (PerFedRec) framework via joint representation learning, user clustering, and model adaptation.

Attribute Clustering +3

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