L
Lianghao Li
Researcher at Hong Kong University of Science and Technology
Publications - 16
Citations - 577
Lianghao Li is an academic researcher from Hong Kong University of Science and Technology. The author has contributed to research in topics: Computer science & Evolutionary algorithm. The author has an hindex of 7, co-authored 9 publications receiving 497 citations. Previous affiliations of Lianghao Li include Tsinghua University.
Papers
More filters
Proceedings Article
Lifelong Machine Learning Systems: Beyond Learning Algorithms
TL;DR: It is proposed that it is now appropriate for the AI community to move beyond learning algorithms to more seriously consider the nature of systems that are capable of learning over a lifetime.
Proceedings Article
Topic correlation analysis for cross-domain text classification
TL;DR: A novel approach named Topic Correlation Analysis (TCA), which extracts both the shared and the domain-specific latent features to facilitate effective knowledge transfer, is proposed and the experimental results justify the superiority of the proposed method over the stat-of-the-art baselines.
Proceedings ArticleDOI
Multi-domain active learning for text classification
TL;DR: This paper proposes a novel multi-domain active learning framework to jointly select data instances from all domains with duplicate information considered, and compares its method with the state-of-the-art active learning approaches on several text classification tasks.
Proceedings Article
Multi-domain active learning for recommendation
TL;DR: The proposed active learning strategy simultaneously considers both specific and independent knowledge over all domains and uses the expected entropy to measure the generalization error of the domain-specific and domain-independent knowledge.
Proceedings Article
Celebrity recommendation with collaborative social topic regression
TL;DR: A unified hierarchical Bayesian model to recommend celebrities to the general users to improve the prediction ability and recommendation interpretability by regularizing celebrity factors through celebrity's social network and descriptive words associated with each celebrity is proposed.