454 citations · 2.5k across the 132 of their papers we have counts for
14 papers · 1 filter
Black-box Optimizer with Implicit Natural Gradient
Yueming Lyu, Ivor W. Tsang
Black-box optimization is primarily important for many compute-intensive applications, including reinforcement learning (RL), robot control, etc. This paper presents a novel theore…
Domain-adversarial Network Alignment
Huiting Hong, Xin Li, Yuangang Pan +1
Network alignment is a critical task to a wide variety of fields. Many existing works leverage on representation learning to accomplish this task without eliminating domain represe…
Node Attribute Generation on Graphs
Xu Chen, Siheng Chen, Huangjie Zheng +4
Graph structured data provide two-fold information: graph structures and node attributes. Numerous graph-based algorithms rely on both information to achieve success in supervised…
Latent Adversarial Defence with Boundary-guided Generation
Xiaowei Zhou, Ivor W. Tsang, Jie Yin
Deep Neural Networks (DNNs) have recently achieved great success in many tasks, which encourages DNNs to be widely used as a machine learning service in model sharing scenarios. Ho…
Probabilistic CCA with Implicit Distributions
Yaxin Shi, Yuangang Pan, Donna Xu +1
Canonical Correlation Analysis (CCA) is a classic technique for multi-view data analysis. To overcome the deficiency of linear correlation in practical multi-view learning tasks, v…
Improving Generalization via Attribute Selection on Out-of-the-box Data
Xiaofeng Xu, Ivor W. Tsang, Chuancai Liu
Zero-shot learning (ZSL) aims to recognize unseen objects (test classes) given some other seen objects (training classes), by sharing information of attributes between different ob…
Learning Image-Specific Attributes by Hyperbolic Neighborhood Graph Propagation
Xiaofeng Xu, Ivor W. Tsang, Xiaofeng Cao +2
As a kind of semantic representation of visual object descriptions, attributes are widely used in various computer vision tasks. In most of existing attribute-based research, class…
Marginalized Average Attentional Network for Weakly-Supervised Learning
Yuan Yuan, Yueming Lyu, Xi Shen +2
In weakly-supervised temporal action localization, previous works have failed to locate dense and integral regions for each entire action due to the overestimation of the most sali…
Fast and Robust Rank Aggregation against Model Misspecification
Yuangang Pan, Weijie Chen, Gang Niu +2
In rank aggregation (RA), a collection of preferences from different users are summarized into a total order under the assumption of homogeneity of users. Model misspecification in…
Curriculum Loss: Robust Learning and Generalization against Label Corruption
Yueming Lyu, Ivor W. Tsang
Deep neural networks (DNNs) have great expressive power, which can even memorize samples with wrong labels. It is vitally important to reiterate robustness and generalization in DN…
Efficient Batch Black-box Optimization with Deterministic Regret Bounds
Yueming Lyu, Yuan Yuan, Ivor W. Tsang
In this work, we investigate black-box optimization from the perspective of frequentist kernel methods. We propose a novel batch optimization algorithm, which jointly maximizes the…
Safeguarded Dynamic Label Regression for Generalized Noisy Supervision
Jiangchao Yao, Ya Zhang, Ivor W. Tsang +1
Learning with noisy labels, which aims to reduce expensive labors on accurate annotations, has become imperative in the Big Data era. Previous noise transition based method has ach…
How does Disagreement Help Generalization against Label Corruption?
Xingrui Yu, Bo Han, Jiangchao Yao +3
Learning with noisy labels is one of the hottest problems in weakly-supervised learning. Based on memorization effects of deep neural networks, training on small-loss instances bec…
A Survey on Multi-output Learning
Donna Xu, Yaxin Shi, Ivor W. Tsang +3
Multi-output learning aims to simultaneously predict multiple outputs given an input. It is an important learning problem due to the pressing need for sophisticated decision making…