32 citations · 92 across the 12 of their papers we have counts for
12 papers
Defense Against Explanation Manipulation
Ruixiang Tang, Ninghao Liu, Fan Yang +2
Explainable machine learning attracts increasing attention as it improves transparency of models, which is helpful for machine learning to be trusted in real applications. However,…
ExAD: An Ensemble Approach for Explanation-based Adversarial Detection
Raj Vardhan, Ninghao Liu, Phakpoom Chinprutthiwong +4
Recent research has shown Deep Neural Networks (DNNs) to be vulnerable to adversarial examples that induce desired misclassifications in the models. Such risks impede the applicati…
Dynamic Memory based Attention Network for Sequential Recommendation
Qiaoyu Tan, Jianwei Zhang, Ninghao Liu +4
Sequential recommendation has become increasingly essential in various online services. It aims to model the dynamic preferences of users from their historical interactions and pre…
Sparse-Interest Network for Sequential Recommendation
Qiaoyu Tan, Jianwei Zhang, Jiangchao Yao +4
Recent methods in sequential recommendation focus on learning an overall embedding vector from a user's behavior sequence for the next-item recommendation. However, from empirical…
Are Interpretations Fairly Evaluated? A Definition Driven Pipeline for Post-Hoc Interpretability
Ninghao Liu, Yunsong Meng, Xia Hu +2
Recent years have witnessed an increasing number of interpretation methods being developed for improving transparency of NLP models. Meanwhile, researchers also try to answer the q…
Explainable Recommender Systems via Resolving Learning Representations
Ninghao Liu, Yong Ge, Li Li +3
Recommender systems play a fundamental role in web applications in filtering massive information and matching user interests. While many efforts have been devoted to developing mor…
Adversarial Attacks and Defenses: An Interpretation Perspective
Ninghao Liu, Mengnan Du, Ruocheng Guo +2
Despite the recent advances in a wide spectrum of applications, machine learning models, especially deep neural networks, have been shown to be vulnerable to adversarial attacks. A…
Learning to Hash with Graph Neural Networks for Recommender Systems
Qiaoyu Tan, Ninghao Liu, Xing Zhao +3
Graph representation learning has attracted much attention in supporting high quality candidate search at scale. Despite its effectiveness in learning embedding vectors for objects…
Is a Single Vector Enough? Exploring Node Polysemy for Network Embedding
Ninghao Liu, Qiaoyu Tan, Yuening Li +3
Networks have been widely used as the data structure for abstracting real-world systems as well as organizing the relations among entities. Network embedding models are powerful to…
Deep Representation Learning for Social Network Analysis
Qiaoyu Tan, Ninghao Liu, Xia Hu
Social network analysis is an important problem in data mining. A fundamental step for analyzing social networks is to encode network data into low-dimensional representations, i.e…
Continuous-time Duality for Super-replication with Transient Price Impact
Peter Bank, Yan Dolinsky
We establish a super-replication duality in a continuous-time financial model where an investor's trades adversely affect bid- and ask-prices for a risky asset and where market res…
Contextual Outlier Interpretation
Ninghao Liu, Donghwa Shin, Xia Hu
Outlier detection plays an essential role in many data-driven applications to identify isolated instances that are different from the majority. While many statistical learning and…