activity
20182020
most citedNetwork On Network for Tabular Data Classification in Real-world Applications

34 citations · 42 across the 2 of their papers we have counts for

collaborators

5 papers

cs.LG202034 cited

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…

cs.LG20198 cited

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,…

cs.LG2019

AutoSF: Searching Scoring Functions for Knowledge Graph Embedding

Yongqi Zhang, Quanming Yao, Wenyuan Dai +1

Scoring functions (SFs), which measure the plausibility of triplets in knowledge graph (KG), have become the crux of KG embedding. Lots of SFs, which target at capturing different…

cs.LG2019

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…

cs.LG2018

Differential Private Stack Generalization with an Application to Diabetes Prediction

Quanming Yao, Xiawei Guo, James T. Kwok +4

To meet the standard of differential privacy, noise is usually added into the original data, which inevitably deteriorates the predicting performance of subsequent learning algorit…