19 citations · 35 across the 11 of their papers we have counts for
11 papers
CloudCons: A Comprehensive End-to-End Benchmark for Cloud Resource Consolidation
Xiaobin Zhang, Lefei Shen, Mouxiang Chen +6
Driven by conservative over-provisioning to guarantee service reliability, resource utilization in cloud data centers remains at low levels. To mitigate this, the forecast-then-opt…
TSFMAudit: Data Contamination Auditing in Forecasting Time Series Foundation Models
Hongkai Li, Shifeng Xie, Lefei Shen +7
Time series foundation models (TSFMs) are increasingly pretrained on large corpora, raising concerns that evaluation datasets may have been exposed during pretraining and thus yiel…
VisionTS: Visual Masked Autoencoders Are Free-Lunch Zero-Shot Time Series Forecasters
Mouxiang Chen, Lefei Shen, Zhuo Li +3
Foundation models have emerged as a promising approach in time series forecasting (TSF). Existing approaches either repurpose large language models (LLMs) or build large-scale time…
Towards Explainable Vulnerability Detection with Large Language Models
Qiheng Mao, Zhenhao Li, Xing Hu +3
Software vulnerabilities pose significant risks to the security and integrity of software systems. Although prior studies have explored vulnerability detection using deep learning…
Advancing Graph Representation Learning with Large Language Models: A Comprehensive Survey of Techniques
Qiheng Mao, Zemin Liu, Chenghao Liu +2
The integration of Large Language Models (LLMs) with Graph Representation Learning (GRL) marks a significant evolution in analyzing complex data structures. This collaboration harn…
PEMT: Multi-Task Correlation Guided Mixture-of-Experts Enables Parameter-Efficient Transfer Learning
Zhisheng Lin, Han Fu, Chenghao Liu +2
Parameter-efficient fine-tuning (PEFT) has emerged as an effective method for adapting pre-trained language models to various tasks efficiently. Recently, there has been a growing…
JumpCoder: Go Beyond Autoregressive Coder via Online Modification
Mouxiang Chen, Hao Tian, Zhongxin Liu +2
While existing code large language models (code LLMs) exhibit impressive capabilities in code generation, their autoregressive sequential generation inherently lacks reversibility.…
ULTRA-DP: Unifying Graph Pre-training with Multi-task Graph Dual Prompt
Mouxiang Chen, Zemin Liu, Chenghao Liu +3
Recent research has demonstrated the efficacy of pre-training graph neural networks (GNNs) to capture the transferable graph semantics and enhance the performance of various downst…
Calibration of Time-Series Forecasting: Detecting and Adapting Context-Driven Distribution Shift
Mouxiang Chen, Lefei Shen, Han Fu +3
Recent years have witnessed the success of introducing deep learning models to time series forecasting. From a data generation perspective, we illustrate that existing models are s…
HINormer: Representation Learning On Heterogeneous Information Networks with Graph Transformer
Qiheng Mao, Zemin Liu, Chenghao Liu +1
Recent studies have highlighted the limitations of message-passing based graph neural networks (GNNs), e.g., limited model expressiveness, over-smoothing, over-squashing, etc. To a…
Scalar is Not Enough: Vectorization-based Unbiased Learning to Rank
Mouxiang Chen, Chenghao Liu, Zemin Liu +1
Unbiased learning to rank (ULTR) aims to train an unbiased ranking model from biased user click logs. Most of the current ULTR methods are based on the examination hypothesis (EH),…