15 citations · 19 across the 7 of their papers we have counts for
7 papers
LeAP: Learnable Adaptive Permutation for Feature Selection in Heterogeneous and Sparse Recommender Systems
Yihong Huang, Chen Chu, Fei Chen +3
Modern industrial recommender systems rely on thousands of heterogeneous features -- ranging from low-dimensional scalars (e.g., statistical value) to high-dimensional embeddings (…
DimGrow: Memory-Efficient Field-level Embedding Dimension Search
Yihong Huang, Chen Chu
Key feature fields need bigger embedding dimensionality, others need smaller. This demands automated dimension allocation. Existing approaches, such as pruning or Neural Architectu…
ShuffleGate: Scalable Feature Optimization for Recommender Systems via Batch-wise Sensitivity Learning
Yihong Huang, Chen Chu, Fan Zhang +4
Feature optimization -- specifically Feature Selection (FS) and Dimension Selection (DS) -- is critical for the efficiency and generalization of large-scale recommender systems. Wh…
GradStop: Exploring Training Dynamics in Unsupervised Outlier Detection through Gradient
Yuang Zhang, Liping Wang, Yihong Huang +3
Unsupervised Outlier Detection (UOD) is a critical task in data mining and machine learning, aiming to identify instances that significantly deviate from the majority. Without any…
EntropyStop: Unsupervised Deep Outlier Detection with Loss Entropy
Yihong Huang, Yuang Zhang, Liping Wang +2
Unsupervised Outlier Detection (UOD) is an important data mining task. With the advance of deep learning, deep Outlier Detection (OD) has received broad interest. Most deep UOD mod…
Unleashing the Potential of Unsupervised Deep Outlier Detection through Automated Training Stopping
Yihong Huang, Yuang Zhang, Liping Wang +1
Outlier detection (OD) has received continuous research interests due to its wide applications. With the development of deep learning, increasingly deep OD algorithms are proposed.…
Unsupervised Graph Outlier Detection: Problem Revisit, New Insight, and Superior Method
Yihong Huang, Liping Wang, Fan Zhang +1
A large number of studies on Graph Outlier Detection (GOD) have emerged in recent years due to its wide applications, in which Unsupervised Node Outlier Detection (UNOD) on attribu…