1 code implementation • 25 Feb 2024 • Neng Kai Nigel Neo, Yeon-Chang Lee, Yiqiao Jin, Sang-Wook Kim, Srijan Kumar
The Fair Graph Anomaly Detection (FairGAD) problem aims to accurately detect anomalous nodes in an input graph while ensuring fairness and avoiding biased predictions against individuals from sensitive subgroups such as gender or political leanings.
no code implementations • 21 Feb 2024 • Yiqiao Jin, MinJe Choi, Gaurav Verma, Jindong Wang, Srijan Kumar
Social media platforms are hubs for multimodal information exchange, encompassing text, images, and videos, making it challenging for machines to comprehend the information or emotions associated with interactions in online spaces.
no code implementations • 26 Oct 2023 • Qinlin Zhao, Jindong Wang, Yixuan Zhang, Yiqiao Jin, Kaijie Zhu, Hao Chen, Xing Xie
Large language models (LLMs) have been widely used as agents to complete different tasks, such as personal assistance or event planning.
1 code implementation • 19 Oct 2023 • Yiqiao Jin, Mohit Chandra, Gaurav Verma, Yibo Hu, Munmun De Choudhury, Srijan Kumar
Our findings underscore the pressing need to bolster the cross-lingual capacities of these models, and to provide an equitable information ecosystem accessible to all.
no code implementations • 3 Oct 2023 • Yijia Xiao, Yiqiao Jin, Yushi Bai, Yue Wu, Xianjun Yang, Xiao Luo, Wenchao Yu, Xujiang Zhao, Yanchi Liu, Haifeng Chen, Wei Wang, Wei Cheng
To address this challenge, we introduce Privacy Protection Language Models (PPLM), a novel paradigm for fine-tuning LLMs that effectively injects domain-specific knowledge while safeguarding data privacy.
1 code implementation • 16 Jun 2023 • Changyu Chen, Xiting Wang, Yiqiao Jin, Victor Ye Dong, Li Dong, Jie Cao, Yi Liu, Rui Yan
In reinforcement learning (RL), there are two major settings for interacting with the environment: online and offline.
no code implementations • 24 Nov 2022 • Yiqiao Jin, Xiting Wang, Yaru Hao, Yizhou Sun, Xing Xie
In this paper, we move towards combining large parametric models with non-parametric prototypical networks.
1 code implementation • 15 Oct 2022 • Yiqiao Jin, Yunsheng Bai, Yanqiao Zhu, Yizhou Sun, Wei Wang
In this paper, we formulate the novel problem of code recommendation, whose purpose is to predict the future contribution behaviors of developers given their interaction history, the semantic features of source code, and the hierarchical file structures of projects.
1 code implementation • 13 Sep 2021 • Yiqiao Jin, Xiting Wang, Ruichao Yang, Yizhou Sun, Wei Wang, Hao Liao, Xing Xie
The detection of fake news often requires sophisticated reasoning skills, such as logically combining information by considering word-level subtle clues.
no code implementations • 1 Jan 2021 • Feng Shi, Chen Li, Shijie Bian, Yiqiao Jin, Ziheng Xu, Tian Han, Song-Chun Zhu
The Propositional Satisfiability Problem (SAT), and more generally, the Constraint Satisfaction Problem (CSP), are mathematical questions defined as finding an assignment to a set of objects that satisfies a series of constraints.