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20242026
most citedPreventing the Popular Item Embedding Based Attack in Federated Recommendations

7 citations · 13 across the 16 of their papers we have counts for

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Showing 2025Show all

5 papers · 1 filter

cs.DB2025

TableCopilot: A Table Assistant Empowered by Natural Language Conditional Table Discovery

Lingxi Cui, Guanyu Jiang, Huan Li +3

The rise of LLM has enabled natural language-based table assistants, but existing systems assume users already have a well-formed table, neglecting the challenge of table discovery…

cs.LG2025

FloE: On-the-Fly MoE Inference on Memory-constrained GPU

Yuxin Zhou, Zheng Li, Jun Zhang +5

With the widespread adoption of Mixture-of-Experts (MoE) models, there is a growing demand for efficient inference on memory-constrained devices. While offloading expert parameters…

cs.LG2025

HMI: Hierarchical Knowledge Management for Efficient Multi-Tenant Inference in Pretrained Language Models

Jun Zhang, Jue Wang, Huan Li +6

The significant computational demands of pretrained language models (PLMs), which often require dedicated hardware, present a substantial challenge in serving them efficiently, esp…

cs.IR2025★ 1 cited

NLCTables: A Dataset for Marrying Natural Language Conditions with Table Discovery

Lingxi Cui, Huan Li, Ke Chen +2

With the growing abundance of repositories containing tabular data, discovering relevant tables for in-depth analysis remains a challenging task. Existing table discovery methods p…

cs.CR2025★ 7 cited

Preventing the Popular Item Embedding Based Attack in Federated Recommendations

Jun Zhang, Huan Li, Dazhong Rong +3

Privacy concerns have led to the rise of federated recommender systems (FRS), which can create personalized models across distributed clients. However, FRS is vulnerable to poisoni…