H-Index & Metrics Best Publications

H-Index & Metrics

Discipline name H-index Citations Publications World Ranking National Ranking
Computer Science D-index 78 Citations 34,644 217 World Ranking 495 National Ranking 298

Research.com Recognitions

Awards & Achievements

2016 - Fellow of the American Association for the Advancement of Science (AAAS)

2014 - ACM Fellow For contributions to large-scale data analysis, machine learning and computational mathematics.

2014 - SIAM Fellow For contributions to numerical linear algebra, data analysis, and machine learning.

Overview

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.

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.

Best Publications

Information-theoretic metric learning

Jason V. Davis;Brian Kulis;Prateek Jain;Suvrit Sra.
international conference on machine learning (2007)

2225 Citations

ScaLAPACK Users' Guide

L. S. Blackford;J. Choi;A. Cleary;E. D'Azevedo.
(1987)

2124 Citations

Co-clustering documents and words using bipartite spectral graph partitioning

Inderjit S. Dhillon.
knowledge discovery and data mining (2001)

1990 Citations

Concept Decompositions for Large Sparse Text Data Using Clustering

Inderjit S. Dhillon;Dharmendra S. Modha.
Machine Learning (2001)

1684 Citations

Clustering with Bregman divergences

Arindam Banerjee;Srujana Merugu;Inderjit S. Dhillon;Joydeep Ghosh.
siam international conference on data mining (2004)

1677 Citations

Information-theoretic co-clustering

Inderjit S. Dhillon;Subramanyam Mallela;Dharmendra S. Modha.
knowledge discovery and data mining (2003)

1543 Citations

Kernel k-means: spectral clustering and normalized cuts

Inderjit S. Dhillon;Yuqiang Guan;Brian Kulis.
knowledge discovery and data mining (2004)

1253 Citations

Weighted Graph Cuts without Eigenvectors A Multilevel Approach

I.S. Dhillon;Yuqiang Guan;B. Kulis.
IEEE Transactions on Pattern Analysis and Machine Intelligence (2007)

934 Citations

Clustering on the Unit Hypersphere using von Mises-Fisher Distributions

Arindam Banerjee;Inderjit S. Dhillon;Joydeep Ghosh;Suvrit Sra.
Journal of Machine Learning Research (2005)

827 Citations

A divisive information theoretic feature clustering algorithm for text classification

Inderjit S. Dhillon;Subramanyam Mallela;Rahul Kumar.
Journal of Machine Learning Research (2003)

654 Citations

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