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 Cluster analysis, Artificial intelligence, Mathematical optimization, Algorithm and Data mining.
His study in Artificial intelligence is interdisciplinary in nature, drawing from both Machine learning and Pattern recognition.
His work carried out in the field of Mathematical optimization brings together such families of science as Rate of convergence, Estimator, Sparse approximation and Mahalanobis distance.
The study incorporates disciplines such as Matrix, Matrix completion, Mutual information, Conditional mutual information and Eigenvalues and eigenvectors in addition to Algorithm.
Inderjit S. Dhillon works mostly in the field of Data mining, limiting it down to concerns involving Biclustering and, occasionally, Residue.
His biological study spans a wide range of topics, including Theoretical computer science and Fuzzy clustering.
His most cited work include:
- Information-theoretic metric learning (1617 citations)
- Co-clustering documents and words using bipartite spectral graph partitioning (1478 citations)
- Clustering with Bregman Divergences (1298 citations)
What are the main themes of his work throughout his whole career to date?
His primary areas of investigation include Algorithm, Artificial intelligence, Cluster analysis, Mathematical optimization and Machine learning.
His Algorithm research is multidisciplinary, incorporating elements of Dimension, Matrix and Speedup.
Inderjit S. Dhillon interconnects Eigenvalues and eigenvectors, Combinatorics and Rank in the investigation of issues within Matrix.
Inderjit S. Dhillon combines topics linked to Pattern recognition with his work on Artificial intelligence.
The Cluster analysis study combines topics in areas such as Theoretical computer science and Data mining.
His Mathematical optimization study integrates concerns from other disciplines, such as Estimator, Least squares, Applied mathematics and Convex optimization.
He most often published in these fields:
- Algorithm (32.39%)
- Artificial intelligence (27.04%)
- Cluster analysis (24.53%)
What were the highlights of his more recent work (between 2015-2021)?
- Algorithm (32.39%)
- Artificial intelligence (27.04%)
- Machine learning (15.09%)
In recent papers he was focusing on the following fields of study:
Algorithm, Artificial intelligence, Machine learning, Matrix and Mathematical optimization are his primary areas of study.
His Algorithm research includes elements of Dimension, Inference, Support vector machine, Cluster analysis and Speedup.
His Cluster analysis study combines topics from a wide range of disciplines, such as Disjoint sets, Similarity, Feature learning and Outlier.
His Artificial intelligence research incorporates themes from Submodular set function, Set and Pattern recognition.
His research in Matrix intersects with topics in Singular value decomposition, Logarithm and Rank.
His work on Coordinate descent as part of his general Mathematical optimization study is frequently connected to Multiplier, thereby bridging the divide between different branches of science.
Between 2015 and 2021, his most popular works were:
- Towards Fast Computation of Certified Robustness for ReLU Networks (202 citations)
- Temporal regularized matrix factorization for high-dimensional time series prediction (134 citations)
- Overlapping Community Detection Using Neighborhood-Inflated Seed Expansion (119 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Statistics
Inderjit S. Dhillon spends much of his time researching Artificial intelligence, Machine learning, Algorithm, Artificial neural network and Matrix.
His Artificial intelligence study incorporates themes from Time series and Pattern recognition.
His work deals with themes such as Noise, Mathematical optimization and Speedup, which intersect with Algorithm.
His studies in Matrix integrate themes in fields like Computational complexity theory, Logarithm and Feature vector.
He performs multidisciplinary study in the fields of Quality and Cluster analysis via his papers.
His Cluster analysis research integrates issues from Key and Nonlinear system.
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