The scientist’s investigation covers issues in Artificial intelligence, Algorithm, Machine learning, Kernel method and Combinatorics. His Artificial intelligence study integrates concerns from other disciplines, such as Computer vision and Pattern recognition. His Pattern recognition study combines topics in areas such as Gradient descent and Generalization.
His work carried out in the field of Algorithm brings together such families of science as Spectral clustering, Cluster analysis, Regression and Hilbert space. His Machine learning research is multidisciplinary, incorporating elements of Function and Scale. The concepts of his Kernel method study are interwoven with issues in Applied mathematics and Rademacher complexity.
Olivier Bousquet spends much of his time researching Artificial intelligence, Machine learning, Algorithm, Support vector machine and Discrete mathematics. His study in Artificial intelligence is interdisciplinary in nature, drawing from both Generalization and Pattern recognition. Olivier Bousquet has researched Pattern recognition in several fields, including Artificial neural network and Image.
His research investigates the connection with Machine learning and areas like Divergence which intersect with concerns in Adversarial system, Maxima and minima, Distribution and Generator. His Algorithm research is multidisciplinary, incorporating perspectives in Random variable, Spectral clustering, Cluster analysis, Minimax and Simple. His Support vector machine research integrates issues from Regularization, Model selection and Discrete geometry.
Olivier Bousquet mainly investigates Artificial intelligence, Machine learning, Algorithm, Generalization and Generative grammar. Artificial intelligence and Rank are two areas of study in which he engages in interdisciplinary work. The various areas that Olivier Bousquet examines in his Machine learning study include Measure, Divergence, Principle of compositionality and Benchmark.
His Algorithm study deals with Minimax intersecting with Classifier, Order and Classifier. The Generalization study combines topics in areas such as Concentration inequality, Random variable, Probabilistic logic, Specialization and Pattern recognition. His Generative grammar research incorporates themes from Precision and recall, Perspective, Kullback–Leibler divergence and Key.
His primary scientific interests are in Machine learning, Benchmark, Artificial intelligence, Adaptation and Logarithm. His Divergence research extends to Machine learning, which is thematically connected. His Adaptation research incorporates elements of Representation, Deep learning, Discriminative model and Linear classifier.
Olivier Bousquet has included themes like Concentration inequality, Random variable, Simple, Probabilistic logic and Series in his Logarithm study. He has researched Concentration inequality in several fields, including Algorithm and Generalization. Olivier Bousquet carries out multidisciplinary research, doing studies in Algorithm and Entropy.
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Learning with Local and Global Consistency
Dengyong Zhou;Olivier Bousquet;Thomas N. Lal;Jason Weston.
neural information processing systems (2003)
Choosing Multiple Parameters for Support Vector Machines
Olivier Chapelle;Vladimir Vapnik;Olivier Bousquet;Sayan Mukherjee.
Machine Learning (2002)
The Tradeoffs of Large Scale Learning
Olivier Bousquet;Léon Bottou.
neural information processing systems (2007)
Stability and generalization
Olivier Bousquet;André Elisseeff.
Journal of Machine Learning Research (2002)
Measuring statistical dependence with hilbert-schmidt norms
Arthur Gretton;Olivier Bousquet;Alex Smola;Bernhard Schölkopf.
algorithmic learning theory (2005)
Ranking on Data Manifolds
Dengyong Zhou;Jason Weston;Arthur Gretton;Olivier Bousquet.
neural information processing systems (2003)
Local Rademacher complexities
Peter L. Bartlett;Olivier Bousquet;Shahar Mendelson.
Annals of Statistics (2005)
Theory of classification : a survey of some recent advances
Stéphane Boucheron;Olivier Bousquet;Gábor Lugosi.
Esaim: Probability and Statistics (2005)
Introduction to Statistical Learning Theory
Olivier Bousquet;Stéphane Boucheron;Gábor Lugosi.
Lecture Notes in Computer Science (2004)
Consistency of spectral clustering
U von Luxburg;M Belkin;O Bousquet.
Annals of Statistics (2008)
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