Xian Wu

On the Generation of Medical Question-Answer Pairs

2018 · arXiv (Cornell University)

Question answering (QA) has achieved promising progress recently. However, answering a question in real-world scenarios like the medical domain is still challenging, due to the requirement of external knowledge and the insufficient quantity of high-quality …

Swift

2020

We report on experiences with Swift congestion control in Google datacenters. Swift targets an end-to-end delay by using AIMD control, with pacing under extreme congestion. With accurate RTT measurement and care in reasoning about delay …

CBR: Context Bias aware Recommendation for Debiasing User Modeling and Click Prediction

2022 · Proceedings of the ACM Web Conference 2022

With the prosperity of recommender systems, the biases existing in user behaviors, which may lead to inconsistency between user preference and behavior records, have attracted wide attention. Though large efforts have been made to infer …

Distributionally Robust Sequential Recommnedation

2023

Modeling user sequential behaviors have been demonstrated to be effective in promoting the recommendation performance. While previous work has achieved remarkable successes, they mostly assume that the training and testing distributions are consistent, which may …

LAMEE: A Light All-MLP Framework for TimeSeries Prediction Empowering Recommendations

2023 · Research Square

Abstract Exogenous variables, unrelated to the recommendation system itself, can significantly enhance its performance. Therefore, integrating these time-evolving exogenous variables into a time series and conducting time series predictions can maximize the potential of recommendation …

Generation is better than Modification: Combating High Class Homophily Variance in Graph Anomaly Detection

2024 · arXiv (Cornell University)

Graph-based anomaly detection is currently an important research topic in the field of graph neural networks (GNNs). We find that in graph anomaly detection, the homophily distribution differences between different classes are significantly greater than …

LaRS: Latent Reasoning Skills for Chain-of-Thought Reasoning

2024

Chain-of-thought (CoT) prompting is a popular in-context learning (ICL) approach for large language models (LLMs), especially when tackling complex reasoning tasks.Traditional ICL approaches construct prompts using examples that contain questions similar to the input question.However, …

MMedAgent-RL: Optimizing Multi-Agent Collaboration for Multimodal Medical Reasoning

2025 · arXiv (Cornell University)

Medical Large Vision-Language Models (Med-LVLMs) have shown strong potential in multimodal diagnostic tasks. However, existing single-agent models struggle to generalize across diverse medical specialties, limiting their performance. Recent efforts introduce multi-agent collaboration frameworks inspired by …

Online Purchase Prediction via Multi-Scale Modeling of Behavior Dynamics

2019

Online purchase forecasting is of great importance in e-commerce platforms, which is the basis of how to present personalized interesting product lists to individual customers. However, predicting online purchases is not trivial as it is …

U-BERT: Pre-training User Representations for Improved Recommendation

2021 · Proceedings of the AAAI Conference on Artificial Intelligence

Learning user representation is a critical task for recommendation systems as it can encode user preference for personalized services. User representation is generally learned from behavior data, such as clicking interactions and review comments. However, …

Large language models for generative information extraction: a survey

2024 · Frontiers of Computer Science

Abstract Information Extraction (IE) aims to extract structural knowledge from plain natural language texts. Recently, generative Large Language Models (LLMs) have demonstrated remarkable capabilities in text understanding and generation. As a result, numerous works have …