66 citations · 86 across the 9 of their papers we have counts for
9 papers
Tackling Diverse Minorities in Imbalanced Classification
Kwei-Herng Lai, Daochen Zha, Huiyuan Chen +5
Imbalanced datasets are commonly observed in various real-world applications, presenting significant challenges in training classifiers. When working with large datasets, the imbal…
Adversarial Collaborative Filtering for Free
Huiyuan Chen, Xiaoting Li, Vivian Lai +5
Collaborative Filtering (CF) has been successfully used to help users discover the items of interest. Nevertheless, existing CF methods suffer from noisy data issue, which negative…
Sharpness-Aware Graph Collaborative Filtering
Huiyuan Chen, Chin-Chia Michael Yeh, Yujie Fan +5
Graph Neural Networks (GNNs) have achieved impressive performance in collaborative filtering. However, GNNs tend to yield inferior performance when the distributions of training an…
Towards Generating Adversarial Examples on Mixed-type Data
Han Xu, Menghai Pan, Zhimeng Jiang +4
The existence of adversarial attacks (or adversarial examples) brings huge concern about the machine learning (ML) model's safety issues. For many safety-critical ML tasks, such as…
Transfer Learning via Contextual Invariants for One-to-Many Cross-Domain Recommendation
Adit Krishnan, Mahashweta Das, Mangesh Bendre +2
The rapid proliferation of new users and items on the social web has aggravated the gray-sheep user/long-tail item challenge in recommender systems. Historically, cross-domain co-c…
motif2vec: Motif Aware Node Representation Learning for Heterogeneous Networks
Manoj Reddy Dareddy, Mahashweta Das, Hao Yang
Recent years have witnessed a surge of interest in machine learning on graphs and networks with applications ranging from vehicular network design to IoT traffic management to soci…
Web Item Reviewing Made Easy By Leveraging Available User Feedback
Azade Nazi, Mahashweta Das, Gautam Das
The widespread use of online review sites over the past decade has motivated businesses of all types to possess an expansive arsenal of user feedback to mark their reputation. Thou…
Top-K Product Design Based on Collaborative Tagging Data
Mahashweta Das, Gautam Das, Vagelis Hristidis
The widespread use and popularity of collaborative content sites (e.g., IMDB, Amazon, Yelp, etc.) has created rich resources for users to consult in order to make purchasing decisi…
Who Tags What? An Analysis Framework
Mahashweta Das, Saravanan Thirumuruganathan, Sihem Amer-Yahia +2
The rise of Web 2.0 is signaled by sites such as Flickr, del.icio.us, and YouTube, and social tagging is essential to their success. A typical tagging action involves three compone…