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20242026
most citedA Two-Stage Data Selection Framework for Data-Efficient Model Training on Edge Devices

2 citations · 2 across the 6 of their papers we have counts for

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6 papers

cs.GT2026

Guiding the Recommender: Information-Aware Auto-Bidding for Content Promotion

Yumou Liu, Zhenzhe Zheng, Jiang Rong +3

Modern content platforms offer paid promotion to mitigate cold start by allocating exposure via auctions. Our empirical analysis reveals a counterintuitive flaw in this paradigm: w…

cs.LG2025

TTF: A Trapezoidal Temporal Fusion Framework for LTV Forecasting in Douyin

Yibing Wan, Zhengxiong Guan, Chaoli Zhang +5

In the user growth scenario, Internet companies invest heavily in paid acquisition channels to acquire new users. But sustainable growth depends on acquired users' generating lifet…

cs.LG20252 cited

A Two-Stage Data Selection Framework for Data-Efficient Model Training on Edge Devices

Chen Gong, Rui Xing, Zhenzhe Zheng +1

The demand for machine learning (ML) model training on edge devices is escalating due to data privacy and personalized service needs. However, we observe that current on-device mod…

cs.LG2025

Efficient Distributed Retrieval-Augmented Generation for Enhancing Language Model Performance

Shangyu Liu, Zhenzhe Zheng, Xiaoyao Huang +3

Small language models (SLMs) support efficient deployments on resource-constrained edge devices, but their limited capacity compromises inference performance. Retrieval-augmented g…

cs.CL2025

AdaSkip: Adaptive Sublayer Skipping for Accelerating Long-Context LLM Inference

Zhuomin He, Yizhen Yao, Pengfei Zuo +4

Long-context large language models (LLMs) inference is increasingly critical, motivating a number of studies devoted to alleviating the substantial storage and computational costs…

cs.GT2024

Contextual Generative Auction with Permutation-level Externalities for Online Advertising

Ruitao Zhu, Yangsu Liu, Dagui Chen +7

Online advertising has become a core revenue driver for the internet industry, with ad auctions playing a crucial role in ensuring platform revenue and advertiser incentives. Tradi…