What is he best known for?
The fields of study he is best known for:
- Statistics
- Artificial intelligence
- Machine learning
His primary scientific interests are in Causal model, Machine learning, Artificial intelligence, Conditional probability distribution and Causal inference.
His Causal model study combines topics in areas such as Transfer of learning and Causal structure.
His work carried out in the field of Machine learning brings together such families of science as Classifier and Automatic label placement.
His study in the fields of Classifier and Semi-supervised learning under the domain of Artificial intelligence overlaps with other disciplines such as Overall survival, Brain tumor segmentation and Medicine.
He combines subjects such as Discrete mathematics, Algorithm, Feature and Marginal distribution with his study of Conditional probability distribution.
His Causal inference research includes themes of Causation, Inference, Series and Benchmark.
His most cited work include:
- Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge (493 citations)
- Multi-label learning by exploiting label dependency (317 citations)
- Domain Adaptation under Target and Conditional Shift (276 citations)
What are the main themes of his work throughout his whole career to date?
His primary areas of investigation include Artificial intelligence, Causal model, Machine learning, Causal structure and Algorithm.
His study on Discriminative model is often connected to Domain adaptation as part of broader study in Artificial intelligence.
The concepts of his Causal model study are interwoven with issues in Identification, Econometrics, Identifiability, Constraint and Applied mathematics.
While working in this field, he studies both Machine learning and Function.
His Causal structure study also includes
- Data mining and related Feature,
- Latent variable and Causal graph most often made with reference to Confounding.
His Algorithm research is multidisciplinary, incorporating elements of Matrix and Independent component analysis.
He most often published in these fields:
- Artificial intelligence (33.17%)
- Causal model (27.23%)
- Machine learning (21.29%)
What were the highlights of his more recent work (between 2019-2021)?
- Artificial intelligence (33.17%)
- Machine learning (21.29%)
- Causal structure (18.81%)
In recent papers he was focusing on the following fields of study:
Artificial intelligence, Machine learning, Causal structure, Latent variable and Confounding are his primary areas of study.
His studies deal with areas such as Invariant and Pattern recognition as well as Artificial intelligence.
His Machine learning research incorporates elements of Test and Sample.
As a member of one scientific family, Kun Zhang mostly works in the field of Causal structure, focusing on Causal model and, on occasion, Data set, Identification, Data mining, Conditional independence and Identifiability.
His biological study spans a wide range of topics, including Correctness and Constraint.
The Correctness study which covers Independent component analysis that intersects with Algorithm.
Between 2019 and 2021, his most popular works were:
- Causal Discovery from Heterogeneous/Nonstationary Data (16 citations)
- Domain Adaptation As a Problem of Inference on Graphical Models (6 citations)
- A Causal View on Robustness of Neural Networks (5 citations)
In his most recent research, the most cited papers focused on:
- Statistics
- Artificial intelligence
- Machine learning
Kun Zhang mainly focuses on Artificial intelligence, Causal model, Discriminative model, Pattern recognition and Causal structure.
His Artificial intelligence study incorporates themes from Machine learning and Invariant.
Kun Zhang interconnects Counterfactual thinking, Conditional probability distribution and Generative grammar in the investigation of issues within Machine learning.
His research investigates the connection between Causal model and topics such as Data set that intersect with problems in Measure, Estimation theory, Confounding and Bellman equation.
The various areas that Kun Zhang examines in his Pattern recognition study include Noise, Distribution, Noise reduction and Existential quantification.
His Causal structure research is multidisciplinary, relying on both Data mining, Directed graph, Algebra, Conditional independence and Identifiability.
This overview was generated by a machine learning system which analysed the scientist’s
body of work. If you have any feedback, you can
contact us
here.
