Zobrazeno 1 - 10
of 117
pro vyhledávání: '"Guo, Ruocheng"'
Personalized decision making requires the knowledge of potential outcomes under different treatments, and confidence intervals about the potential outcomes further enrich this decision-making process and improve its reliability in high-stakes scenari
Externí odkaz:
http://arxiv.org/abs/2405.12387
News media has been utilized as a political tool to stray from facts, presenting biased claims without evidence. Amid the COVID-19 pandemic, politically biased news (PBN) has significantly undermined public trust in vaccines, despite strong medical e
Externí odkaz:
http://arxiv.org/abs/2403.04009
Graph contrastive learning (GCL) has emerged as a pivotal technique in the domain of graph representation learning. A crucial aspect of effective GCL is the caliber of generated positive and negative samples, which is intrinsically dictated by their
Externí odkaz:
http://arxiv.org/abs/2402.10468
In Sequential Recommendation Systems, Cross-Entropy (CE) loss is commonly used but fails to harness item confidence scores during training. Recognizing the critical role of confidence in aligning training objectives with evaluation metrics, we propos
Externí odkaz:
http://arxiv.org/abs/2402.08976
Autor:
Zhang, Sheng, Wang, Maolin, Zhao, Yao, Zhuang, Chenyi, Gu, Jinjie, Guo, Ruocheng, Zhao, Xiangyu, Zhang, Zijian, Yin, Hongzhi
In this age where data is abundant, the ability to distill meaningful insights from the sea of information is essential. Our research addresses the computational and resource inefficiencies that current Sequential Recommender Systems (SRSs) suffer fr
Externí odkaz:
http://arxiv.org/abs/2402.00390
Autor:
Wang, Maolin, Pan, Yu, Xu, Zenglin, Guo, Ruocheng, Zhao, Xiangyu, Wang, Wanyu, Wang, Yiqi, Liu, Zitao, Liu, Langming
Temporal Point Processes (TPPs) hold a pivotal role in modeling event sequences across diverse domains, including social networking and e-commerce, and have significantly contributed to the advancement of recommendation systems and information retrie
Externí odkaz:
http://arxiv.org/abs/2402.00388
Autor:
Wang, Maolin, Zhao, Yao, Liu, Jiajia, Chen, Jingdong, Zhuang, Chenyi, Gu, Jinjie, Guo, Ruocheng, Zhao, Xiangyu
The deployment of Large Multimodal Models (LMMs) within AntGroup has significantly advanced multimodal tasks in payment, security, and advertising, notably enhancing advertisement audition tasks in Alipay. However, the deployment of such sizable mode
Externí odkaz:
http://arxiv.org/abs/2312.05795
Knowledge graphs (KGs), which consist of triples, are inherently incomplete and always require completion procedure to predict missing triples. In real-world scenarios, KGs are distributed across clients, complicating completion tasks due to privacy
Externí odkaz:
http://arxiv.org/abs/2311.10341
We study fair machine learning (ML) under predictive uncertainty to enable reliable and trustworthy decision-making. The seminal work of ``equalized coverage'' proposed an uncertainty-aware fairness notion. However, it does not guarantee equal covera
Externí odkaz:
http://arxiv.org/abs/2311.02243
Adopting a two-stage paradigm of pretraining followed by fine-tuning, Pretrained Language Models (PLMs) have achieved substantial advancements in the field of natural language processing. However, in real-world scenarios, data labels are often noisy
Externí odkaz:
http://arxiv.org/abs/2311.01108