34 citations · 46 across the 3 of their papers we have counts for
6 papers
Network On Network for Tabular Data Classification in Real-world Applications
Yuanfei Luo, Hao Zhou, Weiwei Tu +3
Tabular data is the most common data format adopted by our customers ranging from retail, finance to E-commerce, and tabular data classification plays an essential role to their bu…
MixPUL: Consistency-based Augmentation for Positive and Unlabeled Learning
Tong Wei, Feng Shi, Hai Wang +1
Learning from positive and unlabeled data (PU learning) is prevalent in practical applications where only a couple of examples are positively labeled. Previous PU learning studies…
Towards AutoML in the presence of Drift: first results
Jorge G. Madrid, Hugo Jair Escalante, Eduardo F. Morales +5
Research progress in AutoML has lead to state of the art solutions that can cope quite wellwith supervised learning task, e.g., classification with AutoSklearn. However, so far the…
Efficient Neural Architecture Search via Proximal Iterations
Quanming Yao, Ju Xu, Wei-Wei Tu +1
Neural architecture search (NAS) recently attracts much research attention because of its ability to identify better architectures than handcrafted ones. However, many NAS methods,…
AutoCross: Automatic Feature Crossing for Tabular Data in Real-World Applications
Yuanfei Luo, Mengshuo Wang, Hao Zhou +5
Feature crossing captures interactions among categorical features and is useful to enhance learning from tabular data in real-world businesses. In this paper, we present AutoCross,…
AutoML @ NeurIPS 2018 challenge: Design and Results
Hugo Jair Escalante, Wei-Wei Tu, Isabelle Guyon +5
We organized a competition on Autonomous Lifelong Machine Learning with Drift that was part of the competition program of NeurIPS 2018. This data driven competition asked participa…