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- Gábor Lugosi

Discipline name
H-index
Citations
Publications
World Ranking
National Ranking

Mathematics
D-index
52
Citations
20,365
149
World Ranking
473
National Ranking
9

Engineering and Technology
D-index
50
Citations
20,819
133
World Ranking
1473
National Ranking
36

- Statistics
- Artificial intelligence
- Normal distribution

His primary areas of investigation include Artificial intelligence, Discrete mathematics, Applied mathematics, Regret and Combinatorics. His studies deal with areas such as Machine learning, Learning theory and Pattern recognition as well as Artificial intelligence. He works mostly in the field of Applied mathematics, limiting it down to topics relating to Statistics and, in certain cases, Constant and Rate of convergence.

While the research belongs to areas of Regret, Gábor Lugosi spends his time largely on the problem of Mathematical optimization, intersecting his research to questions surrounding Conjecture and Binary number. His biological study spans a wide range of topics, including Poincaré inequality, Logarithm and Probability theory. His research investigates the connection between Probabilistic logic and topics such as Theme that intersect with issues in Repeated game, Information theory and Game theory.

- A Probabilistic Theory of Pattern Recognition (2543 citations)
- Prediction, learning, and games (2247 citations)
- Concentration Inequalities: A Nonasymptotic Theory of Independence (1431 citations)

Combinatorics, Discrete mathematics, Applied mathematics, Regret and Mathematical optimization are his primary areas of study. The concepts of his Combinatorics study are interwoven with issues in Simple and Constant. His Discrete mathematics research integrates issues from Class, Bounded function and Random variable.

Gábor Lugosi combines subjects such as Empirical risk minimization, Kernel, Estimator, Exponential function and Sample with his study of Applied mathematics. His work deals with themes such as Order, Minimax, Sequence, Time horizon and Upper and lower bounds, which intersect with Regret. Many of his studies involve connections with topics such as Logarithm and Mathematical optimization.

- Combinatorics (28.53%)
- Discrete mathematics (18.53%)
- Applied mathematics (19.12%)

- Combinatorics (28.53%)
- Estimator (11.76%)
- Applied mathematics (19.12%)

His scientific interests lie mostly in Combinatorics, Estimator, Applied mathematics, Mathematical optimization and Statistics. In the subject of general Combinatorics, his work in Random graph, Binary logarithm and Vertex is often linked to High probability, thereby combining diverse domains of study. His Estimator study incorporates themes from Random variable, Independent and identically distributed random variables, Multivariate random variable and Multivariate statistics.

His Applied mathematics research is multidisciplinary, relying on both Distribution and Asymptotic distribution. His Mathematical optimization course of study focuses on Regret and Sequence. His studies in Algorithm integrate themes in fields like Additive white Gaussian noise, Linear regression, Markov chain and Artificial intelligence.

- Benign overfitting in linear regression (112 citations)
- Sub-Gaussian mean estimators (75 citations)
- Sub-Gaussian mean estimators (75 citations)

- Statistics
- Artificial intelligence
- Normal distribution

Gábor Lugosi focuses on Estimator, Mathematical optimization, Regret, Random variable and Empirical risk minimization. His work carried out in the field of Estimator brings together such families of science as Bounded function, Multivariate statistics and Applied mathematics. His Applied mathematics study combines topics from a wide range of disciplines, such as Independent and identically distributed random variables and Multivariate random variable.

Gábor Lugosi has included themes like Nash equilibrium, Type, Order and Time horizon in his Regret study. His biological study spans a wide range of topics, including Parametrization and Algorithm, Randomized algorithm, Combinatorics. His research integrates issues of Discrete mathematics, Banach space, Stochastic gradient descent, Minification and Stability in his study of Empirical risk minimization.

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.

A Probabilistic Theory of Pattern Recognition

Luc Devroye;László Györfi;Gábor Lugosi.

**(1996)**

4660 Citations

Prediction, learning, and games

Nicolo Cesa-Bianchi;Gabor Lugosi.

**(2006)**

3641 Citations

Concentration Inequalities: A Nonasymptotic Theory of Independence

Stéphane Boucheron;Gábor Lugosi;Pascal Massart.

**(2013)**

2543 Citations

Combinatorial Methods in Density Estimation

Luc Devroye;Gábor Lugosi.

**(2011)**

857 Citations

Theory of classification : a survey of some recent advances

Stéphane Boucheron;Olivier Bousquet;Gábor Lugosi.

Esaim: Probability and Statistics **(2005)**

690 Citations

Introduction to Statistical Learning Theory

Olivier Bousquet;Stéphane Boucheron;Gábor Lugosi.

Lecture Notes in Computer Science **(2004)**

666 Citations

Consistency of Random Forests and Other Averaging Classifiers

Gérard Biau;Luc Devroye;Gábor Lugosi.

Journal of Machine Learning Research **(2008)**

571 Citations

Combinatorial bandits

Nicolò Cesa-Bianchi;GáBor Lugosi.

Journal of Computer and System Sciences **(2012)**

358 Citations

Model Selection and Error Estimation

Peter L. Bartlett;Stéphane Boucheron;Gábor Lugosi.

conference on learning theory **(2000)**

358 Citations

Ranking and Empirical Minimization of U-statistics

Stéphan Clémençon;Gábor Lugosi;Nicolas Vayatis.

Annals of Statistics **(2008)**

335 Citations

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