4 citations · 4 across the 4 of their papers we have counts for
5 papers
An Ensembled Latent Factor Model via Differential Evolution and Gradient Descent Optimization
Rui Zhang, Jinhang Liu, Wenbo Zhang
High-dimensional and incomplete (HDI) data are prevalent in many real-world big data scenarios. Latent factor models serve as a common representation learning approach, capable of…
PDR: A Plug-and-Play Positional Decay Framework for LLM Pre-training Data Detection
Jinhan Liu, Yibo Yang, Ruiying Lu +4
Detecting pre-training data in Large Language Models (LLMs) is crucial for auditing data privacy and copyright compliance, yet it remains challenging in black-box, zero-shot settin…
Investigating Mask-aware Prototype Learning for Tabular Anomaly Detection
Ruiying Lu, Jinhan Liu, Chuan Du +1
Tabular anomaly detection, which aims at identifying deviant samples, has been crucial in a variety of real-world applications, such as medical disease identification, financial fr…
A Unified Search and Recommendation Framework Based on Multi-Scenario Learning for Ranking in E-commerce
Jinhan Liu, Qiyu Chen, Junjie Xu +3
Search and recommendation (S&R) are the two most important scenarios in e-commerce. The majority of users typically interact with products in S&R scenarios, indicating the need and…
Scrutinize What We Ignore: Reining In Task Representation Shift Of Context-Based Offline Meta Reinforcement Learning
Hai Zhang, Boyuan Zheng, Tianying Ji +4
Offline meta reinforcement learning (OMRL) has emerged as a promising approach for interaction avoidance and strong generalization performance by leveraging pre-collected data and…