What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Statistics
- Machine learning
His primary areas of study are Artificial intelligence, Kernel, Data mining, Density estimation and Machine learning.
His Artificial intelligence study incorporates themes from Optimization problem and Natural language processing.
His research integrates issues of Anomaly detection, Sequence, Outlier and Test set in his study of Kernel.
His Data mining study combines topics from a wide range of disciplines, such as Semi-supervised learning, Social network, Parameterized complexity, Node and Biological network.
His studies deal with areas such as Pattern recognition, Cross-validation and Covariate shift as well as Density estimation.
In his work, Online machine learning is strongly intertwined with Crowdsourcing, which is a subfield of Machine learning.
His most cited work include:
- Marginalized kernels between labeled graphs (655 citations)
- Direct Importance Estimation with Model Selection and Its Application to Covariate Shift Adaptation (540 citations)
- Direct importance estimation for covariate shift adaptation (250 citations)
What are the main themes of his work throughout his whole career to date?
Hisashi Kashima spends much of his time researching Artificial intelligence, Machine learning, Data mining, Algorithm and Crowdsourcing.
His Artificial intelligence research incorporates elements of Structure and Pattern recognition.
His research in Machine learning intersects with topics in Classifier, Probabilistic logic and Causal inference.
His research integrates issues of Mathematical optimization, Kernel and Tree kernel in his study of Algorithm.
His research in Variable kernel density estimation, Radial basis function kernel and Polynomial kernel are components of Kernel.
His Crowdsourcing research incorporates themes from Crowds and Data science.
He most often published in these fields:
- Artificial intelligence (42.51%)
- Machine learning (26.72%)
- Data mining (15.79%)
What were the highlights of his more recent work (between 2018-2021)?
- Artificial intelligence (42.51%)
- Machine learning (26.72%)
- Theoretical computer science (7.69%)
In recent papers he was focusing on the following fields of study:
Hisashi Kashima focuses on Artificial intelligence, Machine learning, Theoretical computer science, Artificial neural network and Algorithm.
Hisashi Kashima is studying Change detection, which is a component of Artificial intelligence.
In the field of Machine learning, his study on Feature learning, Time series and Feature overlaps with subjects such as Counterfactual thinking.
His work deals with themes such as Adversarial system, Graph, Inference, Graph and Robustness, which intersect with Theoretical computer science.
His Artificial neural network study combines topics in areas such as Persistent homology, Linear combination, Topological data analysis, Computation and Pattern recognition.
His work in the fields of Algorithm, such as Coordinate descent, intersects with other areas such as Value.
Between 2018 and 2021, his most popular works were:
- Approximation Ratios of Graph Neural Networks for Combinatorial Problems (33 citations)
- Knowledge Tracing Machines: Factorization Machines for Knowledge Tracing (26 citations)
- Theoretical evidence for adversarial robustness through randomization (20 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Statistics
- Machine learning
Hisashi Kashima mainly investigates Algorithm, Perspective, Theoretical computer science, Robustness and Artificial neural network.
His Algorithm research integrates issues from Feature engineering and Variety.
His work in Theoretical computer science covers topics such as Inference which are related to areas like Upper and lower bounds.
His Robustness research includes elements of Crowdsourcing, Feature extraction, Pattern recognition and Human-in-the-loop.
Hisashi Kashima applies his multidisciplinary studies on Minimum dominating set and Artificial intelligence in his research.
His study in Artificial intelligence is interdisciplinary in nature, drawing from both Virtual learning environment and Dimension.
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