137 citations · 293 across the 8 of their papers we have counts for
8 papers
Multi-Dimensional Ability Diagnosis for Machine Learning Algorithms
Qi Liu, Zheng Gong, Zhenya Huang +5
Machine learning algorithms have become ubiquitous in a number of applications (e.g. image classification). However, due to the insufficient measurement of traditional metrics (e.g…
GraphMI: Extracting Private Graph Data from Graph Neural Networks
Zaixi Zhang, Qi Liu, Zhenya Huang +4
As machine learning becomes more widely used for critical applications, the need to study its implications in privacy turns to be urgent. Given access to the target model and auxil…
Heterogeneous Graph Representation Learning with Relation Awareness
Le Yu, Leilei Sun, Bowen Du +3
Representation learning on heterogeneous graphs aims to obtain meaningful node representations to facilitate various downstream tasks, such as node classification and link predicti…
Hybrid Micro/Macro Level Convolution for Heterogeneous Graph Learning
Le Yu, Leilei Sun, Bowen Du +3
Heterogeneous graphs are pervasive in practical scenarios, where each graph consists of multiple types of nodes and edges. Representation learning on heterogeneous graphs aims to o…
Predicting Temporal Sets with Deep Neural Networks
Le Yu, Leilei Sun, Bowen Du +3
Given a sequence of sets, where each set contains an arbitrary number of elements, the problem of temporal sets prediction aims to predict the elements in the subsequent set. In pr…
Exploiting Cognitive Structure for Adaptive Learning
Qi Liu, Shiwei Tong, Chuanren Liu +4
Adaptive learning, also known as adaptive teaching, relies on learning path recommendation, which sequentially recommends personalized learning items (e.g., lectures, exercises) to…
Skeptical Deep Learning with Distribution Correction
Mingxiao An, Yongzhou Chen, Qi Liu +4
Recently deep neural networks have been successfully used for various classification tasks, especially for problems with massive perfectly labeled training data. However, it is oft…
Seeing the Forest from the Trees in Two Looks: Matrix Sketching by Cascaded Bilateral Sampling
Kai Zhang, Chuanren Liu, Jie Zhang +3
Matrix sketching is aimed at finding close approximations of a matrix by factors of much smaller dimensions, which has important applications in optimization and machine learning.…