791 citations · 2.5k across the 66 of their papers we have counts for
9 papers · 1 filter
Interstellar: Searching Recurrent Architecture for Knowledge Graph Embedding
Yongqi Zhang, Quanming Yao, Lei Chen
Knowledge graph (KG) embedding is well-known in learning representations of KGs. Many models have been proposed to learn the interactions between entities and relations of the trip…
Searching to Exploit Memorization Effect in Learning from Corrupted Labels
Quanming Yao, Hansi Yang, Bo Han +2
Sample selection approaches are popular in robust learning from noisy labels. However, how to properly control the selection process so that deep networks can benefit from the memo…
Efficient Neural Interaction Function Search for Collaborative Filtering
Quanming Yao, Xiangning Chen, James Kwok +2
In collaborative filtering (CF), interaction function (IFC) plays the important role of capturing interactions among items and users. The most popular IFC is the inner product, whi…
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,…
Efficient Low-Rank Semidefinite Programming with Robust Loss Functions
Quanming Yao, Hangsi Yang, En-Liang Hu +1
In real-world applications, it is important for machine learning algorithms to be robust against data outliers or corruptions. In this paper, we focus on improving the robustness o…
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,…
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…
Generalizing from a Few Examples: A Survey on Few-Shot Learning
Yaqing Wang, Quanming Yao, James Kwok +1
Machine learning has been highly successful in data-intensive applications but is often hampered when the data set is small. Recently, Few-Shot Learning (FSL) is proposed to tackle…
NSCaching: Simple and Efficient Negative Sampling for Knowledge Graph Embedding
Yongqi Zhang, Quanming Yao, Yingxia Shao +1
Knowledge Graph (KG) embedding is a fundamental problem in data mining research with many real-world applications. It aims to encode the entities and relations in the graph into lo…