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20212024
most citedVariance-Reduced Heterogeneous Federated Learning via Stratified Client Selection

6 citations · 11 across the 8 of their papers we have counts for

collaborators

8 papers

cs.IR2024

MLoRA: Multi-Domain Low-Rank Adaptive Network for CTR Prediction

Zhiming Yang, Haining Gao, Dehong Gao +5

Click-through rate (CTR) prediction is one of the fundamental tasks in the industry, especially in e-commerce, social media, and streaming media. It directly impacts website revenu…

cs.CL2024

General2Specialized LLMs Translation for E-commerce

Kaidi Chen, Ben Chen, Dehong Gao +6

Existing Neural Machine Translation (NMT) models mainly handle translation in the general domain, while overlooking domains with special writing formulas, such as e-commerce and le…

cs.GT20232 cited

Cross-channel Budget Coordination for Online Advertising System

Guangyuan Shen, Shenjie Sun, Dehong Gao +4

In online advertising (Ad), advertisers are always eager to know how to globally optimize their budget allocation strategies across different channels for more conversions such as…

cs.IR20231 cited

EdgeNet : Encoder-decoder generative Network for Auction Design in E-commerce Online Advertising

Guangyuan Shen, Shengjie Sun, Dehong Gao +3

We present a new encoder-decoder generative network dubbed EdgeNet, which introduces a novel encoder-decoder framework for data-driven auction design in online e-commerce advertisi…

cs.IR2023

Unified Vision-Language Representation Modeling for E-Commerce Same-Style Products Retrieval

Ben Chen, Linbo Jin, Xinxin Wang +3

Same-style products retrieval plays an important role in e-commerce platforms, aiming to identify the same products which may have different text descriptions or images. It can be…

cs.LG20222 cited

Fast Heterogeneous Federated Learning with Hybrid Client Selection

Guangyuan Shen, Dehong Gao, Duanxiao Song +5

Client selection schemes are widely adopted to handle the communication-efficient problems in recent studies of Federated Learning (FL). However, the large variance of the model up…