activity
20202022
most citedRandomized Online CP Decomposition

6 citations · 18 across the 8 of their papers we have counts for

collaborators

11 papers

cs.LG20223 cited

M^4I: Multi-modal Models Membership Inference

Pingyi Hu, Zihan Wang, Ruoxi Sun +2

With the development of machine learning techniques, the attention of research has been moved from single-modal learning to multi-modal learning, as real-world data exist in the fo…

cs.CL20222 cited

Document-aware Positional Encoding and Linguistic-guided Encoding for Abstractive Multi-document Summarization

Congbo Ma, Wei Emma Zhang, Pitawelayalage Dasun Dileepa Pitawela +3

One key challenge in multi-document summarization is to capture the relations among input documents that distinguish between single document summarization (SDS) and multi-document…

q-fin.RM2021

Risk and return prediction for pricing portfolios of non-performing consumer credit

Siyi Wang, Xing Yan, Bangqi Zheng +4

We design a system for risk-analyzing and pricing portfolios of non-performing consumer credit loans. The rapid development of credit lending business for consumers heightens the n…

cs.CR2021

Oriole: Thwarting Privacy against Trustworthy Deep Learning Models

Liuqiao Chen, Hu Wang, Benjamin Zi Hao Zhao +2

Deep Neural Networks have achieved unprecedented success in the field of face recognition such that any individual can crawl the data of others from the Internet without their expl…

cs.LG2021

Delayed Rewards Calibration via Reward Empirical Sufficiency

Yixuan Liu, Hu Wang, Xiaowei Wang +3

Appropriate credit assignment for delay rewards is a fundamental challenge for reinforcement learning. To tackle this problem, we introduce a delay reward calibration paradigm insp…

eess.SY2021

Multi-intersection Traffic Optimisation: A Benchmark Dataset and a Strong Baseline

Hu Wang, Hao Chen, Qi Wu +3

The control of traffic signals is fundamental and critical to alleviate traffic congestion in urban areas. However, it is challenging since traffic dynamics are complicated in real…