791 citations · 2.5k across the 66 of their papers we have counts for
14 papers · 1 filter
LoSAC: An Efficient Local Stochastic Average Control Method for Federated Optimization
Huiming Chen, Huandong Wang, Quanming Yao +3
Federated optimization (FedOpt), which targets at collaboratively training a learning model across a large number of distributed clients, is vital for federated learning. The prima…
Hierarchical Heterogeneous Graph Representation Learning for Short Text Classification
Yaqing Wang, Song Wang, Quanming Yao +1
Short text classification is a fundamental task in natural language processing. It is hard due to the lack of context information and labeled data in practice. In this paper, we pr…
Codabench: Flexible, Easy-to-Use and Reproducible Benchmarking Platform
Zhen Xu, Sergio Escalera, Isabelle Guyon +5
Obtaining standardized crowdsourced benchmark of computational methods is a major issue in data science communities. Dedicated frameworks enabling fair benchmarking in a unified en…
Pooling Architecture Search for Graph Classification
Lanning Wei, Huan Zhao, Quanming Yao +1
Graph classification is an important problem with applications across many domains, like chemistry and bioinformatics, for which graph neural networks (GNNs) have been state-of-the…
TabGNN: Multiplex Graph Neural Network for Tabular Data Prediction
Xiawei Guo, Yuhan Quan, Huan Zhao +3
Tabular data prediction (TDP) is one of the most popular industrial applications, and various methods have been designed to improve the prediction performance. However, existing wo…
Knowledge Graph Reasoning with Relational Digraph
Yongqi Zhang, Quanming Yao
Reasoning on the knowledge graph (KG) aims to infer new facts from existing ones. Methods based on the relational path have shown strong, interpretable, and transferable reasoning…
Property-Aware Relation Networks for Few-Shot Molecular Property Prediction
Yaqing Wang, Abulikemu Abuduweili, Quanming Yao +1
Molecular property prediction plays a fundamental role in drug discovery to identify candidate molecules with target properties. However, molecular property prediction is essential…
Bilinear Scoring Function Search for Knowledge Graph Learning
Yongqi Zhang, Quanming Yao, James Tin-Yau Kwok
Learning embeddings for entities and relations in knowledge graph (KG) have benefited many downstream tasks. In recent years, scoring functions, the crux of KG learning, have been…
Efficient Data-specific Model Search for Collaborative Filtering
Chen Gao, Quanming Yao, Depeng Jin +1
Collaborative filtering (CF), as a fundamental approach for recommender systems, is usually built on the latent factor model with learnable parameters to predict users' preferences…
Efficient Relation-aware Scoring Function Search for Knowledge Graph Embedding
Shimin Di, Quanming Yao, Yongqi Zhang +1
The scoring function, which measures the plausibility of triplets in knowledge graphs (KGs), is the key to ensure the excellent performance of KG embedding, and its design is also…
Searching to Sparsify Tensor Decomposition for N-ary Relational Data
Shimin Di, Quanming Yao, Lei Chen
Tensor, an extension of the vector and matrix to the multi-dimensional case, is a natural way to describe the N-ary relational data. Recently, tensor decomposition methods have bee…
Role-Aware Modeling for N-ary Relational Knowledge Bases
Yu Liu, Quanming Yao, Yong Li
N-ary relational knowledge bases (KBs) represent knowledge with binary and beyond-binary relational facts. Especially, in an n-ary relational fact, the involved entities play diffe…
Search to aggregate neighborhood for graph neural network
Huan Zhao, Quanming Yao, Weiwei Tu
Recent years have witnessed the popularity and success of graph neural networks (GNN) in various scenarios. To obtain data-specific GNN architectures, researchers turn to neural ar…
Topology-aware Tensor Decomposition for Meta-graph Learning
Hansi Yang, Peiyu Zhang, Quanming Yao
Heterogeneous graphs generally refers to graphs with different types of nodes and edges. A common approach for extracting useful information from heterogeneous graphs is to use met…