What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Statistics
- Machine learning
Jean-Yves Audibert spends much of his time researching Mathematical optimization, Computer vision, Artificial intelligence, Minimax and Algorithm.
His studies deal with areas such as Scaling and Identification as well as Mathematical optimization.
His research in the fields of Image texture, Pixel, Segmentation and Image segmentation overlaps with other disciplines such as Edge detection.
His Artificial intelligence study frequently draws connections between related disciplines such as Crowds.
In his study, which falls under the umbrella issue of Minimax, Empirical risk minimization is strongly linked to Margin.
The Algorithm study which covers Bernstein's inequality that intersects with Regret and Logarithm.
His most cited work include:
- Structured Variable Selection with Sparsity-Inducing Norms (414 citations)
- Exploration-exploitation tradeoff using variance estimates in multi-armed bandits (358 citations)
- General Road Detection From a Single Image (315 citations)
What are the main themes of his work throughout his whole career to date?
His primary areas of investigation include Mathematical optimization, Artificial intelligence, Regret, Logarithm and Computer vision.
His studies in Mathematical optimization integrate themes in fields like Order, Scaling and Conjecture.
His research integrates issues of Machine learning and Pattern recognition in his study of Artificial intelligence.
The concepts of his Regret study are interwoven with issues in Mathematical economics, Minimax and Robustness.
Many of his research projects under Computer vision are closely connected to Edge detection with Edge detection, tying the diverse disciplines of science together.
As part of one scientific family, Jean-Yves Audibert deals mainly with the area of Segmentation, narrowing it down to issues related to the Pixel, and often Image texture and Vanishing point.
He most often published in these fields:
- Mathematical optimization (32.89%)
- Artificial intelligence (31.58%)
- Regret (27.63%)
What were the highlights of his more recent work (between 2009-2014)?
- Mathematical optimization (32.89%)
- Regret (27.63%)
- Artificial intelligence (31.58%)
In recent papers he was focusing on the following fields of study:
His primary areas of study are Mathematical optimization, Regret, Artificial intelligence, Logarithm and Computer vision.
His work carried out in the field of Mathematical optimization brings together such families of science as Basis, Function and Conjecture.
His study in Regret is interdisciplinary in nature, drawing from both Mathematical economics, Minimax and Robustness.
His biological study spans a wide range of topics, including Simple, Mathematical proof, Series and Combinatorics.
His work on Pixel, Leverage, Gabor filter and Image texture as part of general Artificial intelligence research is often related to Edge detection, thus linking different fields of science.
His Logarithm research is multidisciplinary, relying on both Robust statistics, Estimator, Multi-armed bandit and Consistency.
Between 2009 and 2014, his most popular works were:
- Structured Variable Selection with Sparsity-Inducing Norms (414 citations)
- General Road Detection From a Single Image (315 citations)
- Density-aware person detection and tracking in crowds (274 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Statistics
- Machine learning
His main research concerns Mathematical optimization, Artificial intelligence, Regret, Logarithm and Computer vision.
His research in Mathematical optimization intersects with topics in Feature selection and Conjecture.
His work on Image texture, Gabor filter, Leverage and Video tracking as part of general Artificial intelligence research is frequently linked to Edge detection, thereby connecting diverse disciplines of science.
His Regret study integrates concerns from other disciplines, such as Combinatorial optimization and Scaling.
Jean-Yves Audibert has researched Logarithm in several fields, including Ordinary least squares, Identification, Robust statistics, Estimator and Linear combination.
His study in Person detection, Crowd density, Image segmentation, Segmentation and Pixel is carried out as part of his Computer vision studies.
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