What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Algorithm
Maria-Florina Balcan mainly investigates Artificial intelligence, Machine learning, Algorithm, Cluster analysis and Theoretical computer science.
In the field of Artificial intelligence, his study on Semi-supervised learning, Active learning, String kernel and Polynomial kernel overlaps with subjects such as Sample complexity.
His Semi-supervised learning research includes themes of Supervised learning and Unsupervised learning, Algorithmic learning theory.
His study in Machine learning is interdisciplinary in nature, drawing from both Variety, Combinatorial auction and Mechanism design.
His work carried out in the field of Algorithm brings together such families of science as Artificial neural network, MNIST database, Mathematical optimization and Reproducing kernel Hilbert space.
His Theoretical computer science research is multidisciplinary, relying on both Radial basis function kernel, Kernel embedding of distributions, Kernel method and Tree kernel.
His most cited work include:
- Agnostic active learning (261 citations)
- Co-Training and Expansion: Towards Bridging Theory and Practice (231 citations)
- Margin based active learning (206 citations)
What are the main themes of his work throughout his whole career to date?
Maria-Florina Balcan spends much of his time researching Algorithm, Cluster analysis, Mathematical optimization, Artificial intelligence and Theoretical computer science.
His Algorithm research incorporates elements of Active learning and Feature vector.
His Cluster analysis research integrates issues from Approximation algorithm and Parameterized complexity.
His studies deal with areas such as Selection and Mechanism design as well as Mathematical optimization.
His Artificial intelligence study incorporates themes from Machine learning and Pattern recognition.
His work on Communication complexity and Concept class is typically connected to Upper and lower bounds and Class as part of general Theoretical computer science study, connecting several disciplines of science.
He most often published in these fields:
- Algorithm (23.77%)
- Cluster analysis (23.32%)
- Mathematical optimization (22.87%)
What were the highlights of his more recent work (between 2017-2021)?
- Mathematical optimization (22.87%)
- Cluster analysis (23.32%)
- Algorithm (23.77%)
In recent papers he was focusing on the following fields of study:
His primary areas of study are Mathematical optimization, Cluster analysis, Algorithm, Piecewise and Regret.
Maria-Florina Balcan has researched Mathematical optimization in several fields, including Common value auction, Selection, Key and Training set.
His Cluster analysis study combines topics from a wide range of disciplines, such as Integer programming, Parameterized complexity and Constant factor.
His Parameterized complexity study integrates concerns from other disciplines, such as Machine learning, Linkage and Data retrieval.
His Regret study also includes
- Meta learning which intersects with area such as Theoretical computer science,
- Gradient based algorithm and related Meta learning, Deep learning and Regularization.
He performs multidisciplinary studies into Artificial intelligence and Convex optimization in his work.
Between 2017 and 2021, his most popular works were:
- Adaptive Gradient-Based Meta-Learning Methods (56 citations)
- Learning to Branch (48 citations)
- Dispersion for Data-Driven Algorithm Design, Online Learning, and Private Optimization (34 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Algorithm
The scientist’s investigation covers issues in Mathematical optimization, Gradient based algorithm, Convex optimization, Artificial intelligence and Regret.
His Mathematical optimization research includes elements of Tree and Feature selection.
His research is interdisciplinary, bridging the disciplines of Machine learning and Artificial intelligence.
His study in Machine learning focuses on Meta learning in particular.
His Regret research focuses on subjects like Meta learning, which are linked to Theoretical computer science.
His Partition research focuses on Tree traversal and how it connects with Cluster analysis.
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