83 citations · 160 across the 7 of their papers we have counts for
8 papers
DivAug: Plug-in Automated Data Augmentation with Explicit Diversity Maximization
Zirui Liu, Haifeng Jin, Ting-Hsiang Wang +2
Human-designed data augmentation strategies have been replaced by automatically learned augmentation policy in the past two years. Specifically, recent work has empirically shown t…
Towards Interaction Detection Using Topological Analysis on Neural Networks
Zirui Liu, Qingquan Song, Kaixiong Zhou +3
Detecting statistical interactions between input features is a crucial and challenging task. Recent advances demonstrate that it is possible to extract learned interactions from tr…
Towards Automated Neural Interaction Discovery for Click-Through Rate Prediction
Qingquan Song, Dehua Cheng, Hanning Zhou +3
Click-Through Rate (CTR) prediction is one of the most important machine learning tasks in recommender systems, driving personalized experience for billions of consumers. Neural ar…
AutoRec: An Automated Recommender System
Ting-Hsiang Wang, Qingquan Song, Xiaotian Han +3
Realistic recommender systems are often required to adapt to ever-changing data and tasks or to explore different models systematically. To address the need, we present AutoRec, an…
Techniques for Automated Machine Learning
Yi-Wei Chen, Qingquan Song, Xia Hu
Automated machine learning (AutoML) aims to find optimal machine learning solutions automatically given a machine learning problem. It could release the burden of data scientists f…
Coupled Variational Recurrent Collaborative Filtering
Qingquan Song, Shiyu Chang, Xia Hu
We focus on the problem of streaming recommender system and explore novel collaborative filtering algorithms to handle the data dynamicity and complexity in a streaming manner. Alt…