What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- The Internet
His primary areas of study are Artificial intelligence, Information retrieval, Machine learning, World Wide Web and Data mining.
Yong Yu has included themes like Domain, Natural language processing and Pattern recognition in his Artificial intelligence study.
His work is dedicated to discovering how Information retrieval, Web page are connected with Ranking, Rank, Folksonomy, Personalized search and Card sorting and other disciplines.
Yong Yu works mostly in the field of Machine learning, limiting it down to topics relating to Test data and, in certain cases, Boosting, Iterative method, Leverage, Generalization error and Supervised learning.
His World Wide Web research integrates issues from Quality and Semantic data model.
He has researched Data mining in several fields, including Scalability, Similarity, Recommender system, Collaborative filtering and Biclustering.
His most cited work include:
- Robust Recovery of Subspace Structures by Low-Rank Representation (2079 citations)
- Robust Subspace Segmentation by Low-Rank Representation (1144 citations)
- Boosting for transfer learning (1114 citations)
What are the main themes of his work throughout his whole career to date?
His main research concerns Artificial intelligence, Information retrieval, Machine learning, Data mining and World Wide Web.
Yong Yu is interested in Reinforcement learning, which is a branch of Artificial intelligence.
His Information retrieval study frequently draws parallels with other fields, such as Web page.
His Machine learning study often links to related topics such as Inference.
His research integrates issues of Cluster analysis, Feature and Feature vector in his study of Data mining.
His study on SPARQL is often connected to Ontology as part of broader study in Semantic Web.
He most often published in these fields:
- Artificial intelligence (37.61%)
- Information retrieval (34.07%)
- Machine learning (22.79%)
What were the highlights of his more recent work (between 2019-2021)?
- Artificial intelligence (37.61%)
- Machine learning (22.79%)
- Reinforcement learning (10.18%)
In recent papers he was focusing on the following fields of study:
Yong Yu mainly investigates Artificial intelligence, Machine learning, Reinforcement learning, Recommender system and Sample.
Yong Yu focuses mostly in the field of Artificial intelligence, narrowing it down to topics relating to Click-through rate and, in certain cases, Key.
His Machine learning research integrates issues from Language model, Sequence, Side information and Benchmark.
The Reinforcement learning study combines topics in areas such as Mathematical optimization, Leverage, Oracle and Trading strategy.
Information retrieval covers Yong Yu research in Recommender system.
In general Information retrieval, his work in Question answering and Information extraction is often linked to Selection linking many areas of study.
Between 2019 and 2021, his most popular works were:
- Towards Making the Most of BERT in Neural Machine Translation (31 citations)
- AutoFIS: Automatic Feature Interaction Selection in Factorization Models for Click-Through Rate Prediction (13 citations)
- AutoFIS: Automatic Feature Interaction Selection in Factorization Models for Click-Through Rate Prediction (11 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- The Internet
His main research concerns Artificial intelligence, Machine learning, Recommender system, Reinforcement learning and Theoretical computer science.
His studies deal with areas such as Sequence and Click-through rate as well as Artificial intelligence.
Yong Yu combines subjects such as Language model and Relation with his study of Machine learning.
His Recommender system study which covers Space that intersects with Discrete space.
He has researched Reinforcement learning in several fields, including User experience design, Representation and Information retrieval.
He works mostly in the field of Noise, limiting it down to topics relating to Deep learning and, in certain cases, Feature selection and Data mining, as a part of the same area of interest.
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